Health monitoring appliance

ABSTRACT

A heart monitoring system for a patient includes one or more wireless nodes forming a wireless network; a wearable appliance having a wireless transceiver to communicate with the one or more wireless nodes; and an analyzer to determine vital signs, the analyzer coupled to the wireless transceiver to receive patient data over the wireless network.

This application is a continuation of application Ser. No. 11/439,631,filed May 24, 2006, now U.S. Pat. No. 7,558,622 and Ser. No. 12/486,810,filed Jun. 18, 2009, now U.S. Pat. No. 8,108,036 the contents of whichare incorporated by reference.

BACKGROUND

This invention relates generally to methods and systems for monitoring aperson.

Stroke is the third-leading cause of death in the United States. Astroke is defined as a sudden loss of brain function caused by ablockage or rupture of a blood vessel to the brain. Approximately150,000 deaths per year are attributed to stroke. It is also the mostcommon neurologic reason for hospitalization. A stroke occurs when ablood vessel (artery) that supplies blood to the brain bursts or isblocked by a blood clot. Within minutes, the nerve cells in that area ofthe brain are damaged, and they may die within a few hours. As a result,the part of the body controlled by the damaged section of the braincannot function properly. Prior to a stroke, a person may have one ormore transient ischemic attacks (TIAs), which are a warning signal thata stroke may soon occur. TIAs are often called mini strokes becausetheir symptoms are similar to those of a stroke. However, unlike strokesymptoms, TIA symptoms usually disappear within 10 to 20 minutes,although they may last up to 24 hours.

Although great strides have been made in the treatment of stroke, theoverall incidence will continue to rise as our population ages. Primaryand secondary prevention of stroke is important to decrease itsincidence and its associated morbidity. The 30-day mortality rate is7.6% for patients with ischemic stroke and 37.5% for those withhemorrhagic stroke.17 Most deaths within the first week are attributableto the severe nature of a stroke, while deaths that occur later areusually the result of complications of the stroke itself or of othercomorbid conditions. Patients with stroke often have systemic vasculardisease; the annual risk of vascular death in stroke patients is greaterthan 3%. Most stroke survivors are left with some disability. Forexample, 48% are hemiparetic at 6 months and 22% cannot walk. As many asone-half of all stroke survivors are partially dependent on others toperform activities of daily living.18 The rate of recurrentnoncardioembolic stroke is 3% to 7% per year. Stroke can be subdividedinto two types: ischemic and hemorrhagic. Ischemic stroke accounts for85% of all cases. In ischemic stroke, interruption of the blood supplyto the brain results in tissue hypoperfusion, hypoxia, and eventual celldeath secondary to a failure of energy production. Three main mechanismsare involved in the development of ischemic stroke, and they areassociated with atherothrombotic, embolic, and small-vessel diseases.Less common causes include coagulopathies, vasculitis, dissection, andvenous thrombosis.

In atherothrombotic disease, lipid deposition leads to the formation ofplaque, which narrows the vessel lumen and results in turbulent bloodflow through the area of stenosis. The turbulence of the flow and theresultant alterations in flow velocities lead to intimal disruption orplaque rupture, both of which activate the clotting cascade. This causesplatelets to become activated and adhere to the plaque surface, wherethey eventually form a fibrin clot. As the lumen of the vessel becomesmore occluded, ischemia develops distal to the obstruction and caneventually lead to an infarction of the tissue that is dependent on theparent vessel for oxygen delivery. Embolic stroke occurs when dislodgedthrombi travel distally and occlude vessels downstream. One-half of allembolic strokes are caused by atrial fibrillation; the rest areattributable to a variety of causes, including (1) left ventriculardysfunction secondary to acute myocardial infarction or severecongestive heart failure, (2) paradoxical emboli secondary to a patentforamen ovale, and (3) atheroemboli. These latter vessel-to-vesselemboli often arise from atherosclerotic lesions in the aortic arch,carotid arteries, and vertebral arteries.

Small-vessel ischemia can occur when microatheromata occlude the orificeof penetrating arteries. Another mechanism is associated withlipohyalinosis, in which pathologic changes in the tunica media and theadventitia of penetrating arteries occur in the presence of chronichypertension. Elevated blood pressure causes endothelial injury thatdisrupts the blood-brain barrier. This in turn leads to a deposition ofplasma proteins and eventually degeneration of the tunica media smoothmuscle. The smooth muscle is replaced with collagenous fibers, whichinhibit the elasticity of the blood vessel. This causes the vessel lumento narrow and eventually activates the clotting cascade, leading tothrombosis. Small-vessel ischemic disease typically results in lacunarinfarcts, which were named for the small “lakes” (lacunae) that arefound at autopsy in affected patients.

Hypoperfusion can occur as a result of (1) atherosclerotic disease thatlimits distal flow or (2) systemic hypotension, such as seen in patientswho experience acute cardiacarrhythmia or cardiac arrest. A reduction incerebral perfusion pressure activates the autoregulatory system. As thesmall arterioles constrict in an attempt to maintain pressure, ischemiacan develop in the distal branches of the vascular tree. Areas of thebrain that lies between two major vascular supplies (eg, the middle andanterior cerebral arteries) is known as a watershed area. These areasare especially prone to ischemia during episodes of systemichypotension.

Hemorrhagic stroke can be further subclassified as intracerebral andsubarachnoid. Intracerebral hemorrhage is the result of the rupture of avessel within the brain parenchyma. The primary causes of these rupturesare hypertension and amyloid angiopathy; secondary precipitating factorsare listed in Table 1. As with ischemic stroke, the location of anintracerebral hemorrhage determines the type of symptoms and thepatient's overall outcome. For example, a small lobar hemorrhage mightcause only a mild headache and subtle motor deficits, while a hemorrhageof the same size in the pons might result in a coma. Outcomes are alsocorrelated with the volume of blood; hemorrhages greater than 60 ml arealmost always fatal, regardless of their location.

Hypertension is a major cause of hemorrhages of the basal ganglia andbrainstem. Chronic hypertension can lead to the formation ofCharcot-Bouchard aneurysms in lipohyalinotic vessels, which can rupture.Common locations of hypertensive hemorrhages include the putamen,caudate, thalamus, pons, and cerebellum. Amyloid angiopathy is a commoncause of lobar hemorrhage (FIG. 5). This disease process occurs in theelderly and is caused by a deposition of beta amyloid sheets in thetunica media of the vessel wall. The deposition of amyloid proteincauses the vessels to become more rigid, fragile, and prone to rupture.Evidence of hemosiderin deposition in other areas of the brain onmagnetic resonance imaging (MRI) might also be seen. This depositionindicates that the patient has experienced previous hemorrhage andprovides indirect support for the presence of amyloid angiopathy;however, pathologic examination can make a definitive diagnosis.

SUMMARY

In one aspect, a monitoring system for a person includes one or morewireless nodes and a stroke sensor coupled to the person and thewireless nodes to determine vital signs.

In another aspect, a heart monitoring system for a patient includes oneor more wireless nodes forming a wireless network; a wearable appliancehaving a wireless transceiver to communicate with the one or morewireless nodes; and an analyzer to determine vital signs, the analyzercoupled to the wireless transceiver to receive patient data over thewireless network.

In another aspect, heart monitoring system for a patient includes one ormore wireless nodes forming a wireless mesh network; a wearableappliance having a wireless transceiver adapted to communicate with theone or more wireless nodes; and a statistical analyzer to determinevital signs that can signal heart attack or stroke attack, thestatistical analyzer coupled to the wireless transceiver to communicatepatient data over the wireless mesh network.

In yet another aspect, a monitoring system for a person includes one ormore wireless nodes and an electromyography (EMG) sensor coupled to theperson and the wireless nodes to determine a stroke attack.

In another aspect, a health care monitoring system for a person includesone or more wireless nodes forming a wireless mesh network; a wearableappliance having a sound transducer coupled to the wireless transceiver;and a bioelectric impedance (BI) sensor coupled to the wireless meshnetwork to communicate BI data over the wireless mesh network.

In a further aspect, a heart monitoring system for a person includes oneor more wireless nodes forming a wireless mesh network and a wearableappliance having a sound transducer coupled to the wireless transceiver;and a heart disease recognizer coupled to the sound transducer todetermine cardiovascular health and to transmit heart sound over thewireless mesh network to a remote listener if the recognizer identifiesa cardiovascular problem. The heart sound being transmitted may becompressed to save transmission bandwidth.

In yet another aspect, a monitoring system for a person includes one ormore wireless nodes; and a wristwatch having a wireless transceiveradapted to communicate with the one or more wireless nodes; and anaccelerometer to detect a dangerous condition and to generate a warningwhen the dangerous condition is detected.

In yet another aspect, a monitoring system for a person includes one ormore wireless nodes forming a wireless mesh network; and a wearableappliance having a wireless transceiver adapted to communicate with theone or more wireless nodes; and a heartbeat detector coupled to thewireless transceiver. The system may also include an accelerometer todetect a dangerous condition such as a falling condition and to generatea warning when the dangerous condition is detected.

Implementations of the above aspect may include one or more of thefollowing. The wristwatch determines position based on triangulation.The wristwatch determines position based on RF signal strength and RFsignal angle. A switch detects a confirmatory signal from the person.The confirmatory signal includes a head movement, a hand movement, or amouth movement. The confirmatory signal includes the person's voice. Aprocessor in the system executes computer readable code to transmit ahelp request to a remote computer. The code can encrypt or scramble datafor privacy. The processor can execute voice over IP (VOIP) code toallow a user and a remote person to audibly communicate with each other.The voice communication system can include Zigbee VOIP or Bluetooth VOIPor 802.XX VOIP. The remote person can be a doctor, a nurse, a medicalassistant, or a caregiver. The system includes code to store and analyzepatient information. The patient information includes medicine takinghabits, eating and drinking habits, sleeping habits, or excise habits. Apatient interface is provided on a user computer for accessinginformation and the patient interface includes in one implementation atouch screen; voice-activated text reading; and one touch telephonedialing. The processor can execute code to store and analyze informationrelating to the person's ambulation. A global positioning system (GPS)receiver can be used to detect movement and where the person falls. Thesystem can include code to map the person's location onto an area forviewing. The system can include one or more cameras positioned tocapture three dimensional (3D) video of the patient; and a servercoupled to the one or more cameras, the server executing code to detecta dangerous condition for the patient based on the 3D video and allow aremote third party to view images of the patient when the dangerouscondition is detected.

In another aspect, a monitoring system for a person includes one or morewireless bases; and a cellular telephone having a wireless transceiveradapted to communicate with the one or more wireless bases; and anaccelerometer to detect a dangerous condition and to generate a warningwhen the dangerous condition is detected.

In yet another aspect, a monitoring system includes one or more camerasto determine a three dimensional (3D) model of a person; means to detecta dangerous condition based on the 3D model; and means to generate awarning when the dangerous condition is detected.

In another aspect, a method to detect a dangerous condition for aninfant includes placing a pad with one or more sensors in the infant'sdiaper; collecting infant vital parameters; processing the vitalparameter to detect SIDS onset; and generating a warning.

Advantages of the system may include one or more of the following. Thesystem detects the warning signs of stroke and prompts the user to reacha health care provider within 2 hours of symptom onset. The systemenables patent to properly manage acute stroke, and the resulting earlytreatment might reduce the degree of morbidity that is associated withfirst-ever strokes.

Other advantages of the invention may include one or more of thefollowing. The system for non-invasively and continually monitors asubject's arterial blood pressure, with reduced susceptibility to noiseand subject movement, and relative insensitivity to placement of theapparatus on the subject. The system does not need frequentrecalibration of the system while in use on the subject.

In particular, it allows patients to conduct a low-cost, comprehensive,real-time monitoring of their blood pressure. Using the web servicessoftware interface, the invention then avails this information tohospitals, home-health care organizations, insurance companies,pharmaceutical agencies conducting clinical trials and otherorganizations. Information can be viewed using an Internet-basedwebsite, a personal computer, or simply by viewing a display on themonitor. Data measured several times each day provide a relativelycomprehensive data set compared to that measured during medicalappointments separated by several weeks or even months. This allows boththe patient and medical professional to observe trends in the data, suchas a gradual increase or decrease in blood pressure, which may indicatea medical condition. The invention also minimizes effects of white coatsyndrome since the monitor automatically makes measurements withbasically no discomfort; measurements are made at the patient's home orwork, rather than in a medical office.

The wearable appliance is small, easily worn by the patient duringperiods of exercise or day-to-day activities, and non-invasivelymeasures blood pressure can be done in a matter of seconds withoutaffecting the patient. An on-board or remote processor can analyze thetime-dependent measurements to generate statistics on a patient's bloodpressure (e.g., average pressures, standard deviation, beat-to-beatpressure variations) that are not available with conventional devicesthat only measure systolic and diastolic blood pressure at isolatedtimes.

The wearable appliance provides an in-depth, cost-effective mechanism toevaluate a patient's cardiac condition. Certain cardiac conditions canbe controlled, and in some cases predicted, before they actually occur.Moreover, data from the patient can be collected and analyzed while thepatient participates in their normal, day-to-day activities.

In cases where the device has fall detection in addition to bloodpressure measurement, other advantages of the invention may include oneor more of the following. The system provides timely assistance andenables elderly and disabled individuals to live relatively independentlives. The system monitors physical activity patterns, detects theoccurrence of falls, and recognizes body motion patterns leading tofalls. Continuous monitoring of patients is done in an accurate,convenient, unobtrusive, private and socially acceptable manner since acomputer monitors the images and human involvement is allowed only underpre-designated events. The patient's privacy is preserved since humanaccess to videos of the patient is restricted: the system only allowshuman viewing under emergency or other highly controlled conditionsdesignated in advance by the user. When the patient is healthy, peoplecannot view the patient's video without the patient's consent. Only whenthe patient's safety is threatened would the system provide patientinformation to authorized medical providers to assist the patient. Whenan emergency occurs, images of the patient and related medical data canbe compiled and sent to paramedics or hospital for proper preparationfor pick up and check into emergency room.

The system allows certain designated people such as a family member, afriend, or a neighbor to informally check on the well-being of thepatient. The system is also effective in containing the spiraling costof healthcare and outpatient care as a treatment modality by providingremote diagnostic capability so that a remote healthcare provider (suchas a doctor, nurse, therapist or caregiver) can visually communicatewith the patient in performing remote diagnosis. The system allowsskilled doctors, nurses, physical therapists, and other scarce resourcesto assist patients in a highly efficient manner since they can do themajority of their functions remotely.

Additionally, a sudden change of activity (or inactivity) can indicate aproblem. The remote healthcare provider may receive alerts over theInternet or urgent notifications over the phone in case of such suddenaccident indicating changes. Reports of health/activity indicators andthe overall well being of the individual can be compiled for the remotehealthcare provider. Feedback reports can be sent to monitored subjects,their designated informal caregiver and their remote healthcareprovider. Feedback to the individual can encourage the individual toremain active. The content of the report may be tailored to the targetrecipient's needs, and can present the information in a formatunderstandable by an elder person unfamiliar with computers, via anappealing patient interface. The remote healthcare provider will haveaccess to the health and well-being status of their patients withoutbeing intrusive, having to call or visit to get such informationinterrogatively. Additionally, remote healthcare provider can receive areport on the health of the monitored subjects that will help themevaluate these individuals better during the short routine check upvisits. For example, the system can perform patient behavior analysissuch as eating/drinking/smoke habits and medication compliance, amongothers.

The patient's home equipment is simple to use and modular to allow forthe accommodation of the monitoring device to the specific needs of eachpatient. Moreover, the system is simple to install. Regular monitoringof the basic wellness parameters provides significant benefits inhelping to capture adverse events sooner, reduce hospital admissions,and improve the effectiveness of medications, hence, lowering patientcare costs and improving the overall quality of care. Suitable users forsuch systems are disease management companies, health insurancecompanies, self-insured employers, medical device manufacturers andpharmaceutical firms.

The system reduces costs by automating data collection and compliancemonitoring, and hence reduce the cost of nurses for hospital and nursinghome applications. At-home vital signs monitoring enables reducedhospital admissions and lower emergency room visits of chronic patients.Operators in the call centers or emergency response units get highquality information to identify patients that need urgent care so thatthey can be treated quickly, safely, and cost effectively. The Web basedtools allow easy access to patient information for authorized partiessuch as family members, neighbors, physicians, nurses, pharmacists,caregivers, and other affiliated parties to improved the Quality of Carefor the patient.

In an on-line pharmacy aspect, a method for providing patient access tomedication includes collecting patient medical information from apatient computer; securing the patient medical information and sendingthe secured patient medical information from the patient computer to aremote computer; remotely examining the patient and reviewing thepatient medical information; generating a prescription for the patientand sending the prescription to a pharmacy; and performing a druginteraction analysis on the prescription.

Implementations of the on-line pharmacy aspect may include one or moreof the following. The medical information can include temperature, EKG,blood pressure, weight, sugar level, image of the patient, or sound ofthe patient. Responses from the patient to a patient medicalquestionnaire can be captured. The doctor can listen to the patient'sorgan with a digital stethoscope, scanning a video of the patient,running a diagnostic test on the patient, verbally communicating withthe patient. The digital stethoscope can be a microphone orpiezeoelectric transducer coupled to the Zigbee network to relay thesound. A plurality of medical rules can be applied to the medicalinformation to arrive at a diagnosis. Genetic tests or pharmacogenetictests can be run on the patient to check compatibility with theprescription. Approval for the prescription can come from one of: adoctor, a physician, a physician assistant, a nurse. The system canmonitor drug compliance, and can automatically ordering a medicationrefill from the pharmacy.

For pharmacy applications, advantages of the pharmacy system may includeone or more of the following. The system shares the patient's medicalhistory and can be updated by a remote physician and the remotedispensing pharmacy. As the doctor and the pharmacy have the same accessto the patient medical history database, patient data is updated in realtime, and is as current and complete as possible. The patient, doctor,pharmacy, and third party testing entities benefit from a uniformpricing structure that is based on the diagnosis and treatment. Thepatient only pays for standard medical treatments for his or herillness. The physician is paid a standard fee which covers the averagework spent with a patient with the specific type of medical situation.The dispensing pharmacy is able to provide the highest level of service,since it is able to double check all medications dispensed to eachpatient along with the optimal way to detect anticipated negative druginteractions. The pricing structure is competitive as physicians do notneed to be distributed physically, and those with specialty areas mayremain centrally located and yet be able to interact electronically withpatients. The system still provides physical access to specialists sincethe patients which are evaluated can be directed to visit a specialistsphysically, when remote review and contact is ineffectual. The on-linepharmacy tracks the specific needs and medical history of each patientand can provide an expert system to advise the patient on proper drugusage and potential drug interactions. The system automates thepurchasing of drugs, pricing the prescription or submission of theclaims to a third party for pricing, entering the complete prescriptionin their computer system, and auditing from third parties which providepayment. The on-line pharmacy provides detailed multimedia guidance orassistance to the patient regarding the filled prescription. The patientcan freely search for answers regarding the use of the filledprescription, its possible side effects, possible interactions withother drugs, possible alternative treatments, etc. The patient cancommunicate using video or VOIP with a remote pharmacist regarding anynumber of questions, and be counseled by the local pharmacist on the useof the filled prescription. Thus, the system minimizes the danger fromharmful side effects or drug interactions by providing patients withfull access to information. The system allows a patient to enjoy theselection and price of a mail-order pharmacy without subjecting thepatient to dangerous interactions or side effects which may occur inunsupervised prescription purchases. The on-line pharmacy offers theselection and benefits of a “central fill” pharmacy method withoutrequiring the local pharmacy to purchase drugs to fill eachprescription, price each prescription, or be subjected to audits fromthird parties who provide payment.

In yet another embodiment, a wireless housing provides one or morebioelectric contacts conveniently positioned to collect bioelectricpatient data. The housing can be a patch, a wristwatch, a band, awristband, a chest band, a leg band, a sock, a glove, a foot pad, ahead-band, an ear-clip, an ear phone, a shower-cap, an armband, anear-ring, eye-glasses, sun-glasses, a belt, a sock, a shirt, a garment,a jewelry, a bed spread, a pillow cover, a pillow, a mattress, a blanketor a sleeping garment such as a pajama. The bed spread, pillow cover,pillow, mattress, blanket or pajama can have bioelectrically conductivecontacts in an array so that the patient can enjoy his/her sleep whilevital parameters can be captured. In one embodiment, an array ofparallel conductive lines can be formed on the housing side that facesthe patient and the electrical signal can be picked up. The datacaptured by the contacts are transmitted over the mesh network such asZigBee to a base station.

In the above embodiments, the base station can perform the bioelectricsignal processing to extract patient parameters from data captured bythe contacts. In this case, the base station may need a DSP or powerfulCPU to perform the calculations. Alternatively, in an ASP model, thebase station can simply compress the data and upload the data to acentral server or server farm for processing and the result of thesignal processing are sent back to the base station for relay to thepatient interface which can be a wrist-watch, a pad, or a band, amongothers, for notification of any warning signs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system for monitoring a person.

FIG. 2A illustrates a process for determining three dimensional (3D)detection.

FIG. 2B shows an exemplary calibration sheet.

FIG. 3 illustrates a process for detecting falls.

FIG. 4 illustrates a process for detecting facial expressions.

FIG. 5 illustrates an exemplary process for determining and gettingassistance for a patient or user.

FIG. 6A shows an exemplary wrist-watch based assistance device.

FIG. 6B shows an exemplary mesh network working with the wearableappliance of FIG. 6A.

FIG. 7 shows an exemplary mesh network in communication with thewrist-watch device of FIG. 6B.

FIGS. 8-14 show various exemplary wearable appliances to monitor apatient.

FIGS. 15A-15B show exemplary systems for performing patient monitoring.

FIG. 15C shows an exemplary interface to monitor a plurality of persons.

FIG. 15D shows an exemplary dash-board that provides summary informationon the status of a plurality of persons.

FIG. 15E shows an exemplary multi-station vital parameter user interfacefor a professional embodiment.

FIG. 15F shows an exemplary trending pattern display.

FIGS. 16A-16B show exemplary blood pressure determination processes.

FIGS. 16C-E shows exemplary stroke determination processes.

FIG. 17A shows an exemplary EMG sensor.

FIGS. 17B-C show exemplary EMG graphs of a patient.

DESCRIPTION

FIG. 1 shows an exemplary patient monitoring system. The system canoperate in a home, a nursing home, or a hospital. In this system, one ormore mesh network appliances 8 are provided to enable wirelesscommunication in the home monitoring system. Appliances 8 in the meshnetwork can include home security monitoring devices, door alarm, windowalarm, home temperature control devices, fire alarm devices, amongothers. Appliances 8 in the mesh network can be one of multiple portablephysiological transducer, such as a blood pressure monitor, heart ratemonitor, weight scale, thermometer, spirometer, single or multiple leadelectrocardiograph (ECG), a pulse oxymeter, a body fat monitor, acholesterol monitor, a signal from a medicine cabinet, a signal from adrug container, a signal from a commonly used appliance such as arefrigerator/stove/oven/washer, or a signal from an exercise machine,such as a heart rate. As will be discussed in more detail below, oneappliance is a patient monitoring device that can be worn by the patientand includes a single or bi-directional wireless communication link,generally identified by the bolt symbol in FIG. 1, for transmitting datafrom the appliances 8 to the local hub or receiving station or basestation server 20 by way of a wireless radio frequency (RF) link using aproprietary or non-proprietary protocol. For example, within a house, auser may have mesh network appliances that detect window and doorcontacts, smoke detectors and motion sensors, video cameras, key chaincontrol, temperature monitors, CO and other gas detectors, vibrationsensors, and others. A user may have flood sensors and other detectorson a boat. An individual, such as an ill or elderly grandparent, mayhave access to a panic transmitter or other alarm transmitter. Othersensors and/or detectors may also be included. The user may registerthese appliances on a central security network by entering theidentification code for each registered appliance/device and/or system.The mesh network can be Zigbee network or 802.15 network. More detailsof the mesh network is shown in FIG. 7 and discussed in more detailbelow.

A plurality of monitoring cameras 10 may be placed in variouspredetermined positions in a home of a patient 30. The cameras 10 can bewired or wireless. For example, the cameras can communicate overinfrared links or over radio links conforming to the 802X (e.g. 802.11A,802.11B, 802.11G, 802.15) standard or the Bluetooth standard to a basestation/server 20 may communicate over various communication links, suchas a direct connection, such a serial connection, USB connection,Firewire connection or may be optically based, such as infrared orwireless based, for example, home RF, IEEE standard 802.11a/b, Bluetoothor the like. In one embodiment, appliances 8 monitor the patient andactivate the camera 10 to capture and transmit video to an authorizedthird party for providing assistance should the appliance 8 detects thatthe user needs assistance or that an emergency had occurred.

The base station/server 20 stores the patient's ambulation pattern andvital parameters and can be accessed by the patient's family members(sons/daughters), physicians, caretakers, nurses, hospitals, and elderlycommunity. The base station/server 20 may communicate with the remoteserver 200 by DSL, T-1 connection over a private communication networkor a public information network, such as the Internet 100, among others.

The patient 30 may wear one or more wearable patient monitoringappliances such as wrist-watches or clip on devices or electronicjewelry to monitor the patient. One wearable appliance such as awrist-watch includes sensors 40, for example devices for sensing ECG,EKG, blood pressure, sugar level, among others. In one embodiment, thesensors 40 are mounted on the patient's wrist (such as a wristwatchsensor) and other convenient anatomical locations. Exemplary sensors 40include standard medical diagnostics for detecting the body's electricalsignals emanating from muscles (EMG and EOG) and brain (EEG) andcardiovascular system (ECG). Leg sensors can include piezoelectricaccelerometers designed to give qualitative assessment of limb movement.Additionally, thoracic and abdominal bands used to measure expansion andcontraction of the thorax and abdomen respectively. A small sensor canbe mounted on the subject's finger in order to detect blood-oxygenlevels and pulse rate. Additionally, a microphone can be attached tothroat and used in sleep diagnostic recordings for detecting breathingand other noise. One or more position sensors can be used for detectingorientation of body (lying on left side, right side or back) duringsleep diagnostic recordings. Each of sensors 40 can individuallytransmit data to the server 20 using wired or wireless transmission.Alternatively, all sensors 40 can be fed through a common bus into asingle transceiver for wired or wireless transmission. The transmissioncan be done using a magnetic medium such as a floppy disk or a flashmemory card, or can be done using infrared or radio network link, amongothers. The sensor 40 can also include an indoor positioning system oralternatively a global position system (GPS) receiver that relays theposition and ambulatory patterns of the patient to the server 20 formobility tracking.

In one embodiment, the sensors 40 for monitoring vital signs areenclosed in a wrist-watch sized case supported on a wrist band. Thesensors can be attached to the back of the case. For example, in oneembodiment, Cygnus' AutoSensor (Redwood City, Calif.) is used as aglucose sensor. A low electric current pulls glucose through the skin.Glucose is accumulated in two gel collection discs in the AutoSensor.The AutoSensor measures the glucose and a reading is displayed by thewatch.

In another embodiment, EKG/ECG contact points are positioned on the backof the wrist-watch case. In yet another embodiment that providescontinuous, beat-to-beat wrist arterial pulse rate measurements, apressure sensor is housed in a casing with a ‘free-floating’ plunger asthe sensor applanates the radial artery. A strap provides a constantforce for effective applanation and ensuring the position of the sensorhousing to remain constant after any wrist movements. The change in theelectrical signals due to change in pressure is detected as a result ofthe piezoresistive nature of the sensor are then analyzed to arrive atvarious arterial pressure, systolic pressure, diastolic pressure, timeindices, and other blood pressure parameters.

The case may be of a number of variations of shape but can beconveniently made a rectangular, approaching a box-like configuration.The wrist-band can be an expansion band or a wristwatch strap ofplastic, leather or woven material. The wrist-band further contains anantenna for transmitting or receiving radio frequency signals. Thewristband and the antenna inside the band are mechanically coupled tothe top and bottom sides of the wrist-watch housing. Further, theantenna is electrically coupled to a radio frequency transmitter andreceiver for wireless communications with another computer or anotheruser. Although a wrist-band is disclosed, a number of substitutes may beused, including a belt, a ring holder, a brace, or a bracelet, amongother suitable substitutes known to one skilled in the art. The housingcontains the processor and associated peripherals to provide thehuman-machine interface. A display is located on the front section ofthe housing. A speaker, a microphone, and a plurality of push-buttonswitches and are also located on the front section of housing. Aninfrared LED transmitter and an infrared LED receiver are positioned onthe right side of housing to enable the watch to communicate withanother computer using infrared transmission.

In another embodiment, the sensors 40 are mounted on the patient'sclothing. For example, sensors can be woven into a single-piece garment(an undershirt) on a weaving machine. A plastic optical fiber can beintegrated into the structure during the fabric production processwithout any discontinuities at the armhole or the seams. Aninterconnection technology transmits information from (and to) sensorsmounted at any location on the body thus creating a flexible “bus”structure. T-Connectors—similar to “button clips” used in clothing—areattached to the fibers that serve as a data bus to carry the informationfrom the sensors (e.g., EKG sensors) on the body. The sensors will pluginto these connectors and at the other end similar T-Connectors will beused to transmit the information to monitoring equipment or personalstatus monitor. Since shapes and sizes of humans will be different,sensors can be positioned on the right locations for all patients andwithout any constraints being imposed by the clothing. Moreover, theclothing can be laundered without any damage to the sensors themselves.In addition to the fiber optic and specialty fibers that serve assensors and data bus to carry sensory information from the wearer to themonitoring devices, sensors for monitoring the respiration rate can beintegrated into the structure.

In another embodiment, instead of being mounted on the patient, thesensors can be mounted on fixed surfaces such as walls or tables, forexample. One such sensor is a motion detector. Another sensor is aproximity sensor. The fixed sensors can operate alone or in conjunctionwith the cameras 10. In one embodiment where the motion detectoroperates with the cameras 10, the motion detector can be used to triggercamera recording. Thus, as long as motion is sensed, images from thecameras 10 are not saved. However, when motion is not detected, theimages are stored and an alarm may be generated. In another embodimentwhere the motion detector operates stand alone, when no motion issensed, the system generates an alarm.

The server 20 also executes one or more software modules to analyze datafrom the patient. A module 50 monitors the patient's vital signs such asECG/EKG and generates warnings should problems occur. In this module,vital signs can be collected and communicated to the server 20 usingwired or wireless transmitters. In one embodiment, the server 20 feedsthe data to a statistical analyzer such as a neural network which hasbeen trained to flag potentially dangerous conditions. The neuralnetwork can be a back-propagation neural network, for example. In thisembodiment, the statistical analyzer is trained with training data wherecertain signals are determined to be undesirable for the patient, givenhis age, weight, and physical limitations, among others. For example,the patient's glucose level should be within a well established range,and any value outside of this range is flagged by the statisticalanalyzer as a dangerous condition. As used herein, the dangerouscondition can be specified as an event or a pattern that can causephysiological or psychological damage to the patient. Moreover,interactions between different vital signals can be accounted for sothat the statistical analyzer can take into consideration instanceswhere individually the vital signs are acceptable, but in certaincombinations, the vital signs can indicate potentially dangerousconditions. Once trained, the data received by the server 20 can beappropriately scaled and processed by the statistical analyzer. Inaddition to statistical analyzers, the server 20 can process vital signsusing rule-based inference engines, fuzzy logic, as well as conventionalif-then logic. Additionally, the server can process vital signs usingHidden Markov Models (HMMs), dynamic time warping, or template matching,among others.

Through various software modules, the system reads video sequence andgenerates a 3D anatomy file out of the sequence. The proper bone andmuscle scene structure are created for head and face. A based profilestock phase shape will be created by this scene structure. Every scenewill then be normalized to a standardized viewport.

A module 52 monitors the patient ambulatory pattern and generateswarnings should the patient's patterns indicate that the patient hasfallen or is likely to fall. 3D detection is used to monitor thepatient's ambulation. In the 3D detection process, by putting 3 or moreknown coordinate objects in a scene, camera origin, view direction andup vector can be calculated and the 3D space that each camera views canbe defined.

In one embodiment with two or more cameras, camera parameters (e.g.field of view) are preset to fixed numbers. Each pixel from each cameramaps to a cone space. The system identifies one or more 3D featurepoints (such as a birthmark or an identifiable body landmark) on thepatient. The 3D feature point can be detected by identifying the samepoint from two or more different angles. By determining the intersectionfor the two or more cones, the system determines the position of thefeature point. The above process can be extended to certain featurecurves and surfaces, e.g. straight lines, arcs; flat surfaces,cylindrical surfaces. Thus, the system can detect curves if a featurecurve is known as a straight line or arc. Additionally, the system candetect surfaces if a feature surface is known as a flat or cylindricalsurface. The further the patient is from the camera, the lower theaccuracy of the feature point determination. Also, the presence of morecameras would lead to more correlation data for increased accuracy infeature point determination. When correlated feature points, curves andsurfaces are detected, the remaining surfaces are detected by texturematching and shading changes. Predetermined constraints are appliedbased on silhouette curves from different views. A different constraintcan be applied when one part of the patient is occluded by anotherobject. Further, as the system knows what basic organic shape it isdetecting, the basic profile can be applied and adjusted in the process.

In a single camera embodiment, the 3D feature point (e.g. a birth mark)can be detected if the system can identify the same point from twoframes. The relative motion from the two frames should be small butdetectable. Other features curves and surfaces will be detectedcorrespondingly, but can be tessellated or sampled to generate morefeature points. A transformation matrix is calculated between a set offeature points from the first frame to a set of feature points from thesecond frame. When correlated feature points, curves and surfaces aredetected, the rest of the surfaces will be detected by texture matchingand shading changes.

Each camera exists in a sphere coordinate system where the sphere origin(0,0,0) is defined as the position of the camera. The system detectstheta and phi for each observed object, but not the radius or size ofthe object. The radius is approximated by detecting the size of knownobjects and scaling the size of known objects to the object whose sizeis to be determined. For example, to detect the position of a ball thatis 10 cm in radius, the system detects the ball and scales otherfeatures based on the known ball size. For human, features that areknown in advance include head size and leg length, among others. Surfacetexture can also be detected, but the light and shade information fromdifferent camera views is removed. In either single or multiple cameraembodiments, depending on frame rate and picture resolution, certainundetected areas such as holes can exist. For example, if the patientyawns, the patient's mouth can appear as a hole in an image. For 3Dmodeling purposes, the hole can be filled by blending neighborhoodsurfaces. The blended surfaces are behind the visible line.

In one embodiment shown in FIG. 2A, each camera is calibrated before 3Ddetection is done. Pseudo-code for one implementation of a cameracalibration process is as follows:

-   -   Place a calibration sheet with known dots at a known distance        (e.g. 1 meter), and perpendicular to a camera view.    -   Take snap shot of the sheet, and correlate the position of the        dots to the camera image:        Dot1(x,y,1)←>pixel(x,y)    -   Place a different calibration sheet that contains known dots at        another different known distance (e.g. 2 meters), and        perpendicular to camera view.

Take another snapshot of the sheet, and correlate the position of thedots to the camera image:Dot2(x,y,2)←>pixel(x,y)

-   -   Smooth the dots and pixels to minimize digitization errors. By        smoothing the map using a global map function, step errors will        be eliminated and each pixel will be mapped to a cone space.    -   For each pixel, draw a line from Dot1 (x, y, z) to Dot2 (x,        y, z) defining a cone center where the camera can view.    -   One smoothing method is to apply a weighted filter for Dot1 and        Dot2. A weight filter can be used. In one example, the following        exemplary filter is applied.

$\begin{matrix}1 & 2 & 1 \\2 & 4 & 2 \\1 & 2 & 1\end{matrix}$

-   -   Assuming Dot1_Left refers to the value of the dot on the left        side of Dot1 and Dot1_Right refers to the value of the dot to        the right of Dot1 and Dot1_Upper refers to the dot above Dot1,        for example, the resulting smoothed Dot1 value is as follows:

1/16 * (Dot 1 * 4 + Dot1_Left * 2 + Dot1_Right * 2 + Dot1_Upper * 2 + Dot1_Down * 2 + Dot1_UpperLeft + Dot1_UpperRight + Dot1_LowerLeft + Dot1_LowerRight)

-   -   Similarly, the resulting smoothed Dot2 value is as follows:

1/16 * (Dot 2 * 4 + Dot2_Left * 2 + Dot2_Right * 2 + Dot2_Upper * 2 + Dot2_Down * 2 + Dot2_UpperLeft + Dot2_UpperRight + Dot2_LowerLeft + Dot2_LowerRight)

In another smoothing method, features from Dot1 sheet are mapped to asub pixel level and features of Dot2 sheet are mapped to a sub pixellevel and smooth them. To illustrate, Dot1 dot center (5, 5, 1) aremapped to pixel (1.05, 2.86), and Dot2 dot center (10, 10, 2) are mappedto pixel (1.15, 2.76). A predetermined correlation function is thenapplied.

FIG. 2B shows an exemplary calibration sheet having a plurality of dots.In this embodiment, the dots can be circular dots and square dots whichare interleaved among each others. The dots should be placed relativelyclose to each other and each dot size should not be too large, so we canhave as many dots as possible in one snapshot. However, the dots shouldnot be placed too close to each other and the dot size should not be toosmall, so they are not identifiable.

A module 54 monitors patient activity and generates a warning if thepatient has fallen. In one implementation, the system detects the speedof center of mass movement. If the center of mass movement is zero for apredetermined period, the patient is either sleeping or unconscious. Thesystem then attempts to signal the patient and receive confirmatorysignals indicating that the patient is conscious. If patient does notconfirm, then the system generates an alarm. For example, if the patienthas fallen, the system would generate an alarm signal that can be sentto friends, relatives or neighbors of the patient. Alternatively, athird party such as a call center can monitor the alarm signal. Besidesmonitoring for falls, the system performs video analysis of the patient.For example, during a particular day, the system can determine theamount of time for exercise, sleep, and entertainment, among others. Thenetwork of sensors in a patient's home can recognize ordinarypatterns—such as eating, sleeping, and greeting visitors—and to alertcaretakers to out-of-the-ordinary ones—such as prolonged inactivity orabsence. For instance, if the patient goes into the bathroom thendisappears off the sensor for 13 minutes and don't show up anywhere elsein the house, the system infers that patient had taken a bath or ashower. However, if a person falls and remains motionless for apredetermined period, the system would record the event and notify adesignated person to get assistance.

A fall detection process (shown in FIG. 3) performs the followingoperations:

-   -   Find floor space area    -   Define camera view background 3D scene    -   Calculate patient's key features    -   Detect fall

In one implementation, pseudo-code for determining the floor space areais as follows:

-   -   1. Sample the camera view space by M by N, e.g. M=1000, N=500.    -   2. Calculate all sample points the 3D coordinates in room        coordinate system; where Z axis is pointing up. Refer to the 3D        detection for how to calculate 3D positions.    -   3. Find the lowest Z value point (Zmin)    -   4. Find all points whose Z values are less than Zmin+Zto1; where        Zto1 is a user adjustable value, e.g. 2 inches.    -   5. If rooms have different elevation levels, then excluding the        lowest Z floor points, repeat step 2, 3 and 4 while keeping the        lowest Z is within Zto12 of previous Z. In this example Zto12=2        feet, which means the floor level difference should be within 2        feet.    -   6. Detect stairs by finding approximate same flat area but        within equal Z differences between them.    -   7. Optionally, additional information from the user can be used        to define floor space more accurately, especially in single        camera system where the coordinates are less accurate, e.g.:        -   a. Import the CAD file from constructors' blue prints.        -   b. Pick regions from the camera space to define the floor,            then use software to calculate its room coordinates.        -   c. User software to find all flat surfaces, e.g. floors,            counter tops, then user pick the ones, which are actually            floors and/or stairs.

In the implementation, pseudo-code for determining the camera viewbackground 3D scene is as follows:

-   -   1. With the same sample points, calculate x, y coordinates and        the Z depth and calculate 3D positions.    -   2. Determine background scene using one the following methods,        among others:        -   a. When there is nobody in the room.        -   b. Retrieve and update the previous calculated background            scene.        -   c. Continuous updating every sample point when the furthest            Z value was found, that is the background value.

In the implementation, pseudo-code for determining key features of thepatient is as follows:

-   -   1. Foreground objects can be extracted by comparing each sample        point's Z value to the background scene point's Z value, if it        is smaller, then it is on the foreground.    -   2. In normal condition, the feet/shoe can be detected by finding        the lowest Z point clouds close the floor in room space, its        color will be extracted.    -   3. In normal condition, the hair/hat can be detected by finding        the highest Z point clouds close the floor in room space, its        color will be extracted.    -   4. The rest of the features can be determined by searching from        either head or toe. E.g, hat, hair, face, eye, mouth, ear,        earring, neck, lipstick, moustache, jacket, limbs, belt, ring,        hand, etc.    -   5. The key dimension of features will be determined by        retrieving the historic stored data or recalculated, e.g., head        size, mouth width, arm length, leg length, waist, etc.    -   6. In abnormal conditions, features can be detected by detect        individual features then correlated them to different body        parts. E.g, if patient's skin is black, we can hardly get a        yellow or white face, by detecting eye and nose, we know which        part is the face, then we can detect other characteristics.

To detect fall, the pseudo-code for the embodiment is as follows:

-   -   1. The fall has to be detected in almost real time by tracking        movements of key features very quickly. E.g. if patient has        black hair/face, track the center of the black blob will know        roughly where his head move to.    -   2. Then the center of mass will be tracked, center of mass is        usually around belly button area, so the belt or borderline        between upper and lower body closed will be good indications.    -   3. Patient's fall always coupled with rapid deceleration of        center of mass. Software can adjust this threshold based on        patient age, height and physical conditions.    -   4. Then if the fall is accidental and patient has difficult to        get up, one or more of following will happen:        -   a. Patient will move very slowly to find support object to            get up.        -   b. Patient will wave hand to camera ask for help. To detect            this condition, the patient hand has to be detected first by            finding a blob of points with his skin color. Hand motion            can be tracked by calculate the motion of the center of the            points, if it swings left and right, it means patient is            waving to camera.        -   c. Patient is unconscious, motionless. To detect this            condition, extract the foreground object, calculate its            motion vectors, if it is within certain tolerance, it means            patient is not moving. In the mean time, test how long it            last, if it past a user defined time threshold, it means            patient is in great danger.

In one embodiment for fall detection, the system determines a patientfall-down as when the patient's knee, butt or hand is on the floor. Thefall action is defined a quick deceleration of center of mass, which isaround belly button area. An accidental fall action is defined when thepatient falls down with limited movement for a predetermined period.

The system monitors the patients' fall relative to a floor. In oneembodiment, the plan of the floor is specified in advance by thepatient. Alternatively, the system can automatically determine the floorlayout by examining the movement of the patient's feet and estimated thesurfaces touched by the feet as the floor.

In one embodiment with in door positioning, the user can create afacsimile of the floor plan during initialization by walking around theperimeter of each room and recording his/her movement through thein-door positioning system and when complete, press a button to indicateto the system the type of room such as living room, bed room, bath room,among others. Also, the user can calibrate the floor level by sittingdown and then standing up (or vice versa) and allowing the accelerometerto sense the floor through the user motion. Periodically, the user canrecalibrate the floor plan and/or the floor level.

The system detects a patient fall by detecting a center of mass of anexemplary feature. Thus, the software can monitor the center of one ormore objects, for example the head and toe, the patient's belt, thebottom line of the shirt, or the top line of the pants.

The detection of the fall can be adjusted based on two thresholds:

-   -   a. Speed of deceleration of the center of mass.    -   b. The amount of time that the patient lies motionless on the        floor after the fall.

In one example, once a stroke occurs, the system detects a slow motionof patient as the patient rests or a quick motion as the patientcollapses. By adjust the sensitivity threshold, the system detectswhether a patient is uncomfortable and ready to rest or collapse.

If the center of mass movement ceases to move for a predeterminedperiod, the system can generate the warning. In another embodiment,before generating the warning, the system can request the patient toconfirm that he or she does not need assistance. The confirmation can bein the form of a button that the user can press to override the warning.Alternatively, the confirmation can be in the form of a single utterancethat is then detected by a speech recognizer.

In another embodiment, the confirmatory signal is a patient gesture. Thepatient can nod his or her head to request help and can shake the headto cancel the help request. Alternatively, the patient can use aplurality of hand gestures to signal to the server 20 the actions thatthe patient desires.

By adding other detecting mechanism such as sweat detection, the systemcan know whether patient is uncomfortable or not. Other items that canbe monitored include chest movement (frequency and amplitude) and restlength when the patient sits still in one area, among others.

Besides monitoring for falls, the system performs video analysis of thepatient. For example, during a particular day, the system can determinethe amount of time for exercise, sleep, entertainment, among others. Thenetwork of sensors in a patient's home can recognize ordinarypatterns—such as eating, sleeping, and greeting visitors—and to alertcaretakers to out-of-the-ordinary ones—such as prolonged inactivity orabsence. For instance, if the patient goes into the bathroom thendisappears off the camera 10 view for a predetermined period and doesnot show up anywhere else in the house, the system infers that patienthad taken a bath or a shower. However, if a person falls and remainsmotionless for a predetermined period, the system would record the eventand notify a designated person to get assistance.

In one embodiment, changes in the patient's skin color can be detectedby measuring the current light environment, properly calibrating colorspace between two photos, and then determining global color changebetween two states. Thus, when the patient's face turn red, based on theredness, a severity level warning is generated.

In another embodiment, changes in the patient's face are detected byanalyzing a texture distortion in the images. If the patient perspiresheavily, the texture will show small glisters, make-up smudges, orsweat/tear drippings. Another example is, when long stretched face willbe detected as texture distortion. Agony will show certain wrinkletexture patterns, among others.

The system can also utilize high light changes. Thus, when the patientsweats or changes facial appearance, different high light areas areshown, glisters reflect light and pop up geometry generates more highlight areas.

A module 62 analyzes facial changes such as facial asymmetries. Thechange will be detected by superimpose a newly acquired 3D anatomystructure to a historical (normal) 3D anatomy structure to detectface/eye sagging or excess stretch of facial muscles.

In one embodiment, the system determines a set of base 3D shapes, whichare a set of shapes which can represent extremes of certain facialeffects, e.g. frown, open mouth, smiling, among others. The rest of the3D face shape can be generated by blending/interpolating these baseshapes by applied different weight to each base shapes.

The base 3D shape can be captured using 1) a 3D camera such as camerasfrom Steinbichler, Genex Technology, Minolta 3D, Olympus 3D or 2) one ormore 2D camera with preset camera field of view (FOV) parameters. Tomake it more accurate, one or more special markers can be placed onpatient's face. For example, a known dimension square stick can beplaced on the forehead for camera calibration purposes.

Using the above 3D detection method, facial shapes are then extracted.The proper features (e.g. a wrinkle) will be detected and attached toeach base shape. These features can be animated or blended by changingthe weight of different shape(s). The proper features change can bedetected and determine what type of facial shape it will be.

Next, the system super-imposes two 3D facial shapes (historical ornormal facial shapes and current facial shapes). By matching featuresand geometry of changing areas on the face, closely blended shapes canbe matched and facial shape change detection can be performed. Byoverlaying the two shapes, the abnormal facial change such as saggingeyes or mouth can be detected.

The above processes are used to determine paralysis of specific regionsof the face or disorders in the peripheral or central nervous system(trigeminal paralysis; CVA, among others). The software also detectseyelid positions for evidence of ptosis (incomplete opening of one orboth eyelids) as a sign of innervation problems (CVA; Horner syndrome,for example). The software also checks eye movements for pathologicalconditions, mainly of neurological origin are reflected in aberrationsin eye movement. Pupil reaction is also checked for abnormal reaction ofthe pupil to light (pupil gets smaller the stronger the light) mayindicate various pathological conditions mainly of the nervous system.In patients treated for glaucoma pupillary status and motion pattern maybe important to the follow-up of adequate treatment. The software alsochecks for asymmetry in tongue movement, which is usually indicative ofneurological problems. Another check is neck veins: Engorgement of theneck veins may be an indication of heart failure or obstruction ofnormal blood flow from the head and upper extremities to the heart. Thesoftware also analyzes the face, which is usually a mirror of theemotional state of the observed subject. Fear, joy, anger, apathy areonly some of the emotions that can be readily detected, facialexpressions of emotions are relatively uniform regardless of age, sex,race, etc. This relative uniformity allows for the creation of computerprograms attempting to automatically diagnose people's emotional states.

Speech recognition is performed to determine a change in the form ofspeech (slurred speech, difficulties in the formation of words, forexample) may indicated neurological problems, such an observation canalso indicate some outward effects of various drugs or toxic agents.

In one embodiment shown in FIG. 4, a facial expression analysis processperforms the following operations:

-   -   Find floor space area    -   Define camera view background 3D scene    -   Calculate patient's key features    -   Extract facial objects    -   Detect facial orientation    -   Detect facial expression

The first three steps are already discussed above. The patient's keyfeatures provide information on the location of the face, and once theface area has been determined, other features can be detected bydetecting relative position to each other and special characteristics ofthe features:

-   -   Eye: pupil can be detected by applying Chamfer matching        algorithm, by using stock pupil objects.    -   Hair: located on the top of the head, using previous stored hair        color to locate the hair point clouds.    -   Birthmarks, wrinkles and tattoos: pre store all these features        then use Chamfer matching to locate them.    -   Nose: nose bridge and nose holes usually show special        characteristics for detection, sometime depend on the view        angle, is side view, special silhouette will be shown.    -   Eye browse, Lips and Moustache: All these features have special        colors, e.g. red lipstick; and base shape, e.g. patient has no        expression with mouth closed. Software will locate these        features by color matching, then try to deform the base shape        based on expression, and match shape with expression, we can        detect objects and expression at the same time.    -   Teeth, earring, necklace: All these features can be detected by        color and style, which will give extra information.

In one implementation, pseudo-code for detecting facial orientation isas follows:

-   -   Detect forehead area    -   Use the previously determined features and superimpose them on        the base face model to detect a patient face orientation.

Depends on where patient is facing, for a side facing view, silhouetteedges will provide unique view information because there is a one to onecorrespondent between the view and silhouette shape.

Once the patient's face has been aligned to the right view, exemplarypseudo code to detect facial expression is as follows:

-   -   1. Detect shape change. The shape can be match by superimpose        different expression shapes to current shape, and judge by        minimum discrepancy. E.g. wide mouth open.    -   2. Detect occlusion. Sometime the expression can be detected by        occlusal of another objects, e.g., teeth show up means mouth is        open.    -   3. Detect texture map change. The expression can relate to        certain texture changes, if patient smile, certain wrinkles        patents will show up.    -   4. Detect highlight change. The expression can relate to certain        high light changes, if patient sweats or cries, different        highlight area will show up.

Speech recognition can be performed in one embodiment to determine achange in the form of speech (slurred speech, difficulties in theformation of words, for example) may indicated neurological problems,such an observation can also indicate some outward effects of variousdrugs or toxic agents.

A module communicates with a third party such as the police department,a security monitoring center, or a call center. The module operates witha POTS telephone and can use a broadband medium such as DSL or cablenetwork if available. The module 80 requires that at least the telephoneis available as a lifeline support. In this embodiment, duplex soundtransmission is done using the POTS telephone network. The broadbandnetwork, if available, is optional for high resolution video and otheradvanced services transmission.

During operation, the module checks whether broadband network isavailable. If broadband network is available, the module 80 allows highresolution video, among others, to be broadcasted directly from theserver 20 to the third party or indirectly from the server 20 to theremote server 200 to the third party. In parallel, the module 80 allowssound to be transmitted using the telephone circuit. In this manner,high resolution video can be transmitted since sound data is separatelysent through the POTS network.

If broadband network is not available, the system relies on the POTStelephone network for transmission of voice and images. In this system,one or more images are compressed for burst transmission, and at therequest of the third party or the remote server 200, the telephone'ssound system is placed on hold for a brief period to allow transmissionof images over the POTS network. In this manner, existing POTS lifelinetelephone can be used to monitor patients. The resolution and quantityof images are selectable by the third party. Thus, using only thelifeline as a communication medium, the person monitoring the patientcan elect to only listen, to view one high resolution image with duplextelephone voice transmission, to view a few low resolution images, toview a compressed stream of low resolution video with digitized voice,among others.

During installation or while no live person in the scene, each camerawill capture its own environment objects and store it as backgroundimages, the software then detect the live person in the scene, changesof the live person, so only the portion of live person will be send tothe local server, other compression techniques will be applied, e.g.send changing file, balanced video streaming based on change.

The local server will control and schedule how the video/picture will besend, e.g. when the camera is view an empty room, no pictures will besent, the local server will also determine which camera is at the rightview, and request only the corresponding video be sent. The local serverwill determine which feature it is interested in looking at, e.g. faceand request only that portion be sent.

With predetermined background images and local server controlledstreaming, the system will enable higher resolution and more camerasystem by using narrower bandwidth.

Through this module, a police officer, a security agent, or a healthcareagent such as a physician at a remote location can engage, ininteractive visual communication with the patient. The patient's healthdata or audio-visual signal can be remotely accessed. The patient alsohas access to a video transmission of the third party. Should thepatient experience health symptoms requiring intervention and immediatecare, the health care practitioner at the central station may summonhelp from an emergency services provider. The emergency servicesprovider may send an ambulance, fire department personnel, familymember, or other emergency personnel to the patient's remote location.The emergency services provider may, perhaps, be an ambulance facility,a police station, the local fire department, or any suitable supportfacility.

Communication between the patient's remote location and the centralstation can be initiated by a variety of techniques. One method is bymanually or automatically placing a call on the telephone to thepatient's home or from the patient's home to the central station.

Alternatively, the system can ask a confirmatory question to the patientthrough text to speech software. The patient can be orally instructed bythe health practitioner to conduct specific physical activities such asspecific arm movements, walking, bending, among others. The examinationbegins during the initial conversation with the monitored subject. Anychanges in the spontaneous gestures of the body, arms and hands duringspeech as well as the fulfillment of nonspecific tasks are importantsigns of possible pathological events. The monitoring person caninstruct the monitored subject to perform a series of simple tasks whichcan be used for diagnosis of neurological abnormalities. Theseobservations may yield early indicators of the onset of a disease.

A network 100 such as the Internet receives images from the server 20and passes the data to one or more remote servers 200. The images aretransmitted from the server 200 over a secure communication link such asvirtual private network (VPN) to the remote server(s) 200.

In one embodiment where cameras are deployed, the server 200 collectsdata from a plurality of cameras and uses the 3D images technology todetermine if the patient needs help. The system can transmit video (liveor archived) to the friend, relative, neighbor, or call center for humanreview. At each viewer site, after a viewer specifies the correct URL tothe client browser computer, a connection with the server 200 isestablished and user identity authenticated using suitable password orother security mechanisms. The server 200 then retrieves the documentfrom its local disk or cache memory storage and transmits the contentover the network. In the typical scenario, the user of a Web browserrequests that a media stream file be downloaded, such as sending, inparticular, the URL of a media redirection file from a Web server. Themedia redirection file (MRF) is a type of specialized Hypertext MarkupLanguage (HTML) file that contains instructions for how to locate themultimedia file and in what format the multimedia file is in. The Webserver returns the MRF file to the user's browser program. The browserprogram then reads the MRF file to determine the location of the mediaserver containing one or more multimedia content files. The browser thenlaunches the associated media player application program and passes theMRF file to it. The media player reads the MRF file to obtain theinformation needed to open a connection to a media server, such as aURL, and the required protocol information, depending upon the type ofmedial content is in the file. The streaming media content file is thenrouted from the media server down to the user.

In the camera embodiment, the transactions between the server 200 andone of the remote servers 200 are detailed. The server 200 compares oneimage frame to the next image frame. If no difference exists, theduplicate frame is deleted to minimize storage space. If a differenceexists, only the difference information is stored as described in theJPEG standard. This operation effectively compresses video informationso that the camera images can be transmitted even at telephone modemspeed of 64 k or less. More aggressive compression techniques can beused. For example, patient movements can be clusterized into a group ofknown motion vectors, and patient movements can be described using a setof vectors. Only the vector data is saved. During view back, each vectoris translated into a picture object which is suitably rasterized. Theinformation can also be compressed as motion information.

Next, the server 200 transmits the compressed video to the remote server200. The server 200 stores and caches the video data so that multipleviewers can view the images at once since the server 200 is connected toa network link such as telephone line modem, cable modem, DSL modem, andATM transceiver, among others.

In one implementation, the servers 200 use RAID-5 striping and paritytechniques to organize data in a fault tolerant and efficient manner.The RAID (Redundant Array of Inexpensive Disks) approach is welldescribed in the literature and has various levels of operation,including RAID-5, and the data organization can achieve data storage ina fault tolerant and load balanced manner. RAID-5 provides that thestored data is spread among three or more disk drives, in a redundantmanner, so that even if one of the disk drives fails, the data stored onthe drive can be recovered in an efficient and error free manner fromthe other storage locations. This method also advantageously makes useof each of the disk drives in relatively equal and substantiallyparallel operations. Accordingly, if one has a six gigabyte clustervolume which spans three disk drives, each disk drive would beresponsible for servicing two gigabytes of the cluster volume. Each twogigabyte drive would be comprised of one-third redundant information, toprovide the redundant, and thus fault tolerant, operation required forthe RAID-5 approach. For additional physical security, the server can bestored in a Fire Safe or other secured box, so there is no chance toerase the recorded data, this is very important for forensic analysis.

The system can also monitor the patient's gait pattern and generatewarnings should the patient's gait patterns indicate that the patient islikely to fall. The system will detect patient skeleton structure,stride and frequency; and based on this information to judge whetherpatient has joint problem, asymmetrical bone structure, among others.The system can store historical gait information, and by overlayingcurrent structure to the historical (normal) gait information, gaitchanges can be detected. In the camera embodiment, an estimate of thegait pattern is done using the camera. In a camera-less embodiment, thegait can be sensed by providing a sensor on the floor and a sensor nearthe head and the variance in the two sensor positions are used toestimate gait characteristics.

The system also provides a patient interface 90 to assist the patient ineasily accessing information. In one embodiment, the patient interfaceincludes a touch screen; voice-activated text reading; one touchtelephone dialing; and video conferencing. The touch screen has largeicons that are pre-selected to the patient's needs, such as his or herfavorite web sites or application programs. The voice activated textreading allows a user with poor eye-sight to get information from thepatient interface 90. Buttons with pre-designated dialing numbers, orvideo conferencing contact information allow the user to call a friendor a healthcare provider quickly.

In one embodiment, medicine for the patient is tracked using radiofrequency identification (RFID) tags. In this embodiment, each drugcontainer is tracked through an RFID tag that is also a drug label. TheRF tag is an integrated circuit that is coupled with a mini-antenna totransmit data. The circuit contains memory that stores theidentification Code and other pertinent data to be transmitted when thechip is activated or interrogated using radio energy from a reader. Areader consists of an RF antenna, transceiver and a micro-processor. Thetransceiver sends activation signals to and receives identification datafrom the tag. The antenna may be enclosed with the reader or locatedoutside the reader as a separate piece. RFID readers communicatedirectly with the RFID tags and send encrypted usage data over thepatient's network to the server 200 and eventually over the Internet100. The readers can be built directly into the walls or the cabinetdoors.

In one embodiment, capacitively coupled RFID tags are used. Thecapacitive RFID tag includes a silicon microprocessor that can store 96bits of information, including the pharmaceutical manufacturer, drugname, usage instruction and a 40-bit serial number. A conductive carbonink acts as the tag's antenna and is applied to a paper substratethrough conventional printing means. The silicon chip is attached toprinted carbon-ink electrodes on the back of a paper label, creating alow-cost, disposable tag that can be integrated on the drug label. Theinformation stored on the drug labels is written in a Medicine MarkupLanguage (MML), which is based on the eXtensible Markup Language (XML).MML would allow all computers to communicate with any computer system ina similar way that Web servers read Hyper Text Markup Language (HTML),the common language used to create Web pages.

After receiving the medicine container, the patient places the medicinein a medicine cabinet, which is also equipped with a tag reader. Thissmart cabinet then tracks all medicine stored in it. It can track themedicine taken, how often the medicine is restocked and can let thepatient know when a particular medication is about to expire. At thispoint, the server 200 can order these items automatically. The server200 also monitors drug compliance, and if the patient does not removethe bottle to dispense medication as prescribed, the server 200 sends awarning to the healthcare provider.

The database tracks typical arm and leg movements to determine whetherthe user is experiencing muscle weakness reflective of a stroke. Ifmuscle weakness is detected, the system presents the user withadditional tests to confirm the likelihood of a stroke attack. If theinformation indicates a stroke had occurred, the system stores the timeof the stroke detection and calls for emergency assistance to get timelytreatment for the stroke. The user's habits and movements can bedetermined by the system for stroke detection. This is done by trackinglocation, ambulatory travel vectors and time in a database. If the usertypically sleeps between 10 pm to 6 am, the location would reflect thatthe user's location maps to the bedroom between 10 pm and 6 am. In oneexemplary system, the system builds a schedule of the user's activity asfollows:

Location Time Start Time End Heart Rate Bed room 10 pm 6 am 60-80  Gymroom 6 am 7 am 90-120 Bath room 7 am 7:30 am 85-120 Dining room 7:30 am8:45 am 80-90  Home Office 8:45 am 11:30 am 85-100 . . . . . .

The habit tracking is adaptive in that it gradually adjusts to theuser's new habits. If there are sudden changes, the system flags thesesudden changes for follow up. For instance, if the user spends threehours in the bathroom, the system prompts the third party (such as acall center) to follow up with the patient to make sure he or she doesnot need help.

In one embodiment, data driven analyzers may be used to track thepatient's habits. These data driven analyzers may incorporate a numberof models such as parametric statistical models, non-parametricstatistical models, clustering models, nearest neighbor models,regression methods, and engineered (artificial) neural networks. Priorto operation, data driven analyzers or models of the patient's habits orambulation patterns are built using one or more training sessions. Thedata used to build the analyzer or model in these sessions are typicallyreferred to as training data. As data driven analyzers are developed byexamining only training examples, the selection of the training data cansignificantly affect the accuracy and the learning speed of the datadriven analyzer. One approach used heretofore generates a separate dataset referred to as a test set for training purposes. The test set isused to avoid overfitting the model or analyzer to the training data.Overfitting refers to the situation where the analyzer has memorized thetraining data so well that it fails to fit or categorize unseen data.Typically, during the construction of the analyzer or model, theanalyzer's performance is tested against the test set. The selection ofthe analyzer or model parameters is performed iteratively until theperformance of the analyzer in classifying the test set reaches anoptimal point. At this point, the training process is completed. Analternative to using an independent training and test set is to use amethodology called cross-validation. Cross-validation can be used todetermine parameter values for a parametric analyzer or model for anon-parametric analyzer. In cross-validation, a single training data setis selected. Next, a number of different analyzers or models are builtby presenting different parts of the training data as test sets to theanalyzers in an iterative process. The parameter or model structure isthen determined on the basis of the combined performance of all modelsor analyzers. Under the cross-validation approach, the analyzer or modelis typically retrained with data using the determined optimal modelstructure.

In general, multiple dimensions of a user's daily activities such asstart and stop times of interactions of different interactions areencoded as distinct dimensions in a database. A predictive model,including time series models such as those employing autoregressionanalysis and other standard time series methods, dynamic Bayesiannetworks and Continuous Time Bayesian Networks, or temporalBayesian-network representation and reasoning methodology, is built, andthen the model, in conjunction with a specific query makes targetinferences.

Bayesian networks provide not only a graphical, easily interpretablealternative language for expressing background knowledge, but they alsoprovide an inference mechanism; that is, the probability of arbitraryevents can be calculated from the model. Intuitively, given a Bayesiannetwork, the task of mining interesting unexpected patterns can berephrased as discovering item sets in the data which are much more—ormuch less—frequent than the background knowledge suggests. These casesare provided to a learning and inference subsystem, which constructs aBayesian network that is tailored for a target prediction. The Bayesiannetwork is used to build a cumulative distribution over events ofinterest.

In another embodiment, a genetic algorithm (GA) search technique can beused to find approximate solutions to identifying the user's habits.Genetic algorithms are a particular class of evolutionary algorithmsthat use techniques inspired by evolutionary biology such asinheritance, mutation, natural selection, and recombination (orcrossover). Genetic algorithms are typically implemented as a computersimulation in which a population of abstract representations (calledchromosomes) of candidate solutions (called individuals) to anoptimization problem evolves toward better solutions. Traditionally,solutions are represented in binary as strings of 0s and 1s, butdifferent encodings are also possible. The evolution starts from apopulation of completely random individuals and happens in generations.In each generation, the fitness of the whole population is evaluated,multiple individuals are stochastically selected from the currentpopulation (based on their fitness), modified (mutated or recombined) toform a new population, which becomes current in the next iteration ofthe algorithm.

Substantially any type of learning system or process may be employed todetermine the user's ambulatory and living patterns so that unusualevents can be flagged.

In one embodiment, clustering operations are performed to detectpatterns in the data. In another embodiment, a neural network is used torecognize each pattern as the neural network is quite robust atrecognizing user habits or patterns. Once the treatment features havebeen characterized, the neural network then compares the input userinformation with stored templates of treatment vocabulary known by theneural network recognizer, among others. The recognition models caninclude a Hidden Markov Model (HMM), a dynamic programming model, aneural network, a fuzzy logic, or a template matcher, among others.These models may be used singly or in combination.

Dynamic programming considers all possible points within the permitteddomain for each value of i. Because the best path from the current pointto the next point is independent of what happens beyond that point.Thus, the total cost of [i(k), j(k)] is the cost of the point itselfplus the cost of the minimum path to it. Preferably, the values of thepredecessors can be kept in an M×N array, and the accumulated cost keptin a 2×N array to contain the accumulated costs of the immediatelypreceding column and the current column. However, this method requiressignificant computing resources. For the recognizer to find the optimaltime alignment between a sequence of frames and a sequence of nodemodels, it must compare most frames against a plurality of node models.One method of reducing the amount of computation required for dynamicprogramming is to use pruning Pruning terminates the dynamic programmingof a given portion of user habit information against a given treatmentmodel if the partial probability score for that comparison drops below agiven threshold. This greatly reduces computation.

Considered to be a generalization of dynamic programming, a hiddenMarkov model is used in the preferred embodiment to evaluate theprobability of occurrence of a sequence of observations O(1), O(2), . .. O(t), . . . , O(T), where each observation O(t) may be either adiscrete symbol under the VQ approach or a continuous vector. Thesequence of observations may be modeled as a probabilistic function ofan underlying Markov chain having state transitions that are notdirectly observable. In one embodiment, the Markov network is used tomodel a number of user habits and activities. The transitions betweenstates are represented by a transition matrix A=[a(i,j)]. Each a(i,j)term of the transition matrix is the probability of making a transitionto state j given that the model is in state i. The output symbolprobability of the model is represented by a set of functions B=[b(j)(O(t)], where the b(j) (O(t) term of the output symbol matrix is theprobability of outputting observation O(t), given that the model is instate j. The first state is always constrained to be the initial statefor the first time frame of the utterance, as only a prescribed set ofleft to right state transitions are possible. A predetermined finalstate is defined from which transitions to other states cannot occur.Transitions are restricted to reentry of a state or entry to one of thenext two states. Such transitions are defined in the model as transitionprobabilities. Although the preferred embodiment restricts the flowgraphs to the present state or to the next two states, one skilled inthe art can build an HMM model without any transition restrictions,although the sum of all the probabilities of transitioning from anystate must still add up to one. In each state of the model, the currentfeature frame may be identified with one of a set of predefined outputsymbols or may be labeled probabilistically. In this case, the outputsymbol probability b(j) O(t) corresponds to the probability assigned bythe model that the feature frame symbol is O(t). The model arrangementis a matrix A=[a(i,j)] of transition probabilities and a technique ofcomputing B=b(j) O(t), the feature frame symbol probability in state j.The Markov model is formed for a reference pattern from a plurality ofsequences of training patterns and the output symbol probabilities aremultivariate Gaussian function probability densities. The patient habitinformation is processed by a feature extractor. During learning, theresulting feature vector series is processed by a parameter estimator,whose output is provided to the hidden Markov model. The hidden Markovmodel is used to derive a set of reference pattern templates, eachtemplate representative of an identified pattern in a vocabulary set ofreference treatment patterns. The Markov model reference templates arenext utilized to classify a sequence of observations into one of thereference patterns based on the probability of generating theobservations from each Markov model reference pattern template. Duringrecognition, the unknown pattern can then be identified as the referencepattern with the highest probability in the likelihood calculator. TheHMM template has a number of states, each having a discrete value.However, because treatment pattern features may have a dynamic patternin contrast to a single value. The addition of a neural network at thefront end of the HMM in an embodiment provides the capability ofrepresenting states with dynamic values. The input layer of the neuralnetwork comprises input neurons. The outputs of the input layer aredistributed to all neurons in the middle layer. Similarly, the outputsof the middle layer are distributed to all output states, which normallywould be the output layer of the neuron. However, each output hastransition probabilities to itself or to the next outputs, thus forminga modified HMM. Each state of the thus formed HMM is capable ofresponding to a particular dynamic signal, resulting in a more robustHMM. Alternatively, the neural network can be used alone withoutresorting to the transition probabilities of the HMM architecture.

The system allows patients to conduct a low-cost, comprehensive,real-time monitoring of their vital parameters such as ambulation andfalls. Information can be viewed using an Internet-based website, apersonal computer, or simply by viewing a display on the monitor. Datameasured several times each day provide a relatively comprehensive dataset compared to that measured during medical appointments separated byseveral weeks or even months. This allows both the patient and medicalprofessional to observe trends in the data, such as a gradual increaseor decrease in blood pressure, which may indicate a medical condition.The invention also minimizes effects of white coat syndrome since themonitor automatically makes measurements with basically no discomfort;measurements are made at the patient's home or work, rather than in amedical office.

The wearable appliance is small, easily worn by the patient duringperiods of exercise or day-to-day activities, and non-invasivelymeasures blood pressure can be done in a matter of seconds withoutaffecting the patient. An on-board or remote processor can analyze thetime-dependent measurements to generate statistics on a patient's bloodpressure (e.g., average pressures, standard deviation, beat-to-beatpressure variations) that are not available with conventional devicesthat only measure systolic and diastolic blood pressure at isolatedtimes.

The wearable appliance provides an in-depth, cost-effective mechanism toevaluate a patient's health condition. Certain cardiac conditions can becontrolled, and in some cases predicted, before they actually occur.Moreover, data from the patient can be collected and analyzed while thepatient participates in their normal, day-to-day activities.

Software programs associated with the Internet-accessible website,secondary software system, and the personal computer analyze the bloodpressure, and heart rate, and pulse oximetry values to characterize thepatient's cardiac condition. These programs, for example, may provide areport that features statistical analysis of these data to determineaverages, data displayed in a graphical format, trends, and comparisonsto doctor-recommended values.

When the appliance cannot communicate with the mesh network, theappliance simply stores information in memory and continues to makemeasurements. The watch component automatically transmits all the storedinformation (along with a time/date stamp) when it comes in proximity tothe wireless mesh network, which then transmits the information throughthe wireless network.

In one embodiment, the server provides a web services that communicatewith third party software through an interface. To generate vitalparameters such as blood pressure information for the web servicessoftware interface, the patient continuously wears the blood-pressuremonitor for a short period of time, e.g. one to two weeks after visitinga medical professional during a typical ‘check up’ or after signing upfor a short-term monitoring program through the website. In this case,the wearable device such as the watch measures mobility through theaccelerometer and blood pressure in a near-continuous, periodic mannersuch as every fifteen minutes. This information is then transmitted overthe mesh network to a base station that communicates over the Internetto the server.

To view information sent from the blood-pressure monitor and falldetector on the wearable appliance, the patient or an authorized thirdparty such as family members, emergency personnel, or medicalprofessional accesses a patient user interface hosted on the web server200 through the Internet 100 from a remote computer system. The patientinterface displays vital information such as ambulation, blood pressureand related data measured from a single patient. The system may alsoinclude a call center, typically staffed with medical professionals suchas doctors, nurses, or nurse practioners, whom access a care-providerinterface hosted on the same website on the server 200. Thecare-provider interface displays vital data from multiple patients.

The wearable appliance has an indoor positioning system and processesthese signals to determine a location (e.g., latitude, longitude, andaltitude) of the monitor and, presumably, the patient. This locationcould be plotted on a map by the server, and used to locate a patientduring an emergency, e.g. to dispatch an ambulance.

In one embodiment, the web page hosted by the server 200 includes aheader field that lists general information about the patient (e.g.name, age, and ID number, general location, and information concerningrecent measurements); a table that lists recently measured bloodpressure data and suggested (i.e. doctor-recommended) values of thesedata; and graphs that plot the systolic and diastolic blood pressuredata in a time-dependent manner. The header field additionally includesa series of tabs that each link to separate web pages that include,e.g., tables and graphs corresponding to a different data measured bythe wearable device such as calorie consumption/dissipation, ambulationpattern, sleeping pattern, heart rate, pulse oximetry, and temperature.The table lists a series of data fields that show running average valuesof the patient's daily, monthly, and yearly vital parameters. The levelsare compared to a series of corresponding ‘suggested’ values of vitalparameters that are extracted from a database associated with the website. The suggested values depend on, among other things, the patient'sage, sex, and weight. The table then calculates the difference betweenthe running average and suggested values to give the patient an idea ofhow their data compares to that of a healthy patient. The web softwareinterface may also include security measures such as authentication,authorization, encryption, credential presentation, and digitalsignature resolution. The interface may also be modified to conform toindustry-mandated, XML schema definitions, while being ‘backwardscompatible’ with any existing XML schema definitions.

The system provides for self-registration of appliances by the user.Data can be synchronized between the Repository and appliance(s) via thebase station 20. The user can preview the readings received from theappliance(s) and reject erroneous readings. The user or treatingprofessional can set up the system to generate alerts against receiveddata, based on pre-defined parameters. The system can determine trendsin received data, based on user defined parameters.

Appliance registration is the process by which a patient monitoringappliance is associated with one or more users of the system. Thismechanism is also used when provisioning appliances for a user by athird party, such as a clinician (or their respective delegate). In oneimplementation, the user (or delegate) logs into the portal to selectone or more appliances and available for registration. In turn, the basestation server 20 broadcasts a query to all nodes in the mesh network toretrieve identification information for the appliance such asmanufacturer information, appliance model information, appliance serialnumber and optionally a hub number (available on hub packaging). Theuser may register more than one appliance at this point. The systemoptionally sets up a service subscription for appliance(s) usage. Thisincludes selecting service plans and providing payment information. Theappliance(s) are then associated with this user's account and a controlfile with appliance identification information is synchronized betweenthe server 200 and the base station 20 and each appliance oninitialization. In one embodiment, each appliance 8 transmits data tothe base station 20 in an XML format for ease of interfacing and iseither kept encrypted or in a non-readable format on the base station 20for security reasons.

The base station 20 frequently collects and synchronizes data from theappliances 8. The base station 20 may use one of various transportationmethods to connect to the repository on the server 200 using a PC asconduit or through a connection established using an embedded modem(connected to a phone line), a wireless router (DSL or cable wirelessrouter), a cellular modem, or another network-connected appliance (suchas, but not limited to, a web-phone, video-phone, embedded computer, PDAor handheld computer).

In one embodiment, users may set up alerts or reminders that aretriggered when one or more reading meet a certain set of conditions,depending on parameters defined by the user. The user chooses thecondition that they would like to be alerted to and by providing theparameters (e.g. threshold value for the reading) for alert generation.Each alert may have an interval which may be either the number of datapoints or a time duration in units such as hours, days, weeks or months.The user chooses the destination where the alert may be sent. Thisdestination may include the user's portal, e-mail, pager, voice-mail orany combination of the above.

Trends are determined by applying mathematical and statistical rules(e.g. moving average and deviation) over a set of reading values. Eachrule is configurable by parameters that are either automaticallycalculated or are set by the user.

The user may give permission to others as needed to read or edit theirpersonal data or receive alerts. The user or clinician could have a listof people that they want to monitor and have it show on their “MyAccount” page, which serves as a local central monitoring station in oneembodiment. Each person may be assigned different access rights whichmay be more or less than the access rights that the patient has. Forexample, a doctor or clinician could be allowed to edit data for exampleto annotate it, while the patient would have read-only privileges forcertain pages. An authorized person could set the reminders and alertsparameters with limited access to others. In one embodiment, the basestation server 20 serves a web page customized by the user or the user'srepresentative as the monitoring center that third parties such asfamily, physicians, or caregivers can log in and access information. Inanother embodiment, the base station 20 communicates with the server 200at a call center so that the call center provides all services. In yetanother embodiment, a hybrid solution where authorized representativescan log in to the base station server 20 access patient informationwhile the call center logs into both the server 200 and the base stationserver 20 to provide complete care services to the patient.

The server 200 may communicate with a business process outsourcing (BPO)company or a call center to provide central monitoring in an environmentwhere a small number of monitoring agents can cost effectively monitormultiple people 24 hours a day. A call center agent, a clinician or anursing home manager may monitor a group or a number of users via asummary “dashboard” of their readings data, with ability to drill-downinto details for the collected data. A clinician administrator maymonitor the data for and otherwise administer a number of users of thesystem. A summary “dashboard” of readings from all Patients assigned tothe Administrator is displayed upon log in to the Portal by theAdministrator. Readings may be color coded to visually distinguishnormal vs. readings that have generated an alert, along with descriptionof the alert generated. The Administrator may drill down into thedetails for each Patient to further examine the readings data, viewcharts etc. in a manner similar to the Patient's own use of the system.The Administrator may also view a summary of all the appliancesregistered to all assigned Patients, including but not limited to allappliance identification information. The Administrator has access onlyto information about Patients that have been assigned to theAdministrator by a Super Administrator. This allows for segmenting theentire population of monitored Patients amongst multiple Administrators.The Super Administrator may assign, remove and/or reassign Patientsamongst a number of Administrators.

In one embodiment, a patient using an Internet-accessible computer andweb browser, directs the browser to an appropriate URL and signs up fora service for a short-term (e.g., 1 month) period of time. The companyproviding the service completes an accompanying financial transaction(e.g. processes a credit card), registers the patient, and ships thepatient a wearable appliance for the short period of time. Theregistration process involves recording the patient's name and contactinformation, a number associated with the monitor (e.g. a serialnumber), and setting up a personalized website. The patient then usesthe monitor throughout the monitoring period, e.g. while working,sleeping, and exercising. During this time the monitor measures datafrom the patient and wirelessly transmits it through the channel to adata center. There, the data are analyzed using software running oncomputer servers to generate a statistical report. The computer serversthen automatically send the report to the patient using email, regularmail, or a facsimile machine at different times during the monitoringperiod. When the monitoring period is expired, the patient ships thewearable appliance back to the monitoring company.

Different web pages may be designed and accessed depending on theend-user. As described above, individual users have access to web pagesthat only their ambulation and blood pressure data (i.e., the patientinterface), while organizations that support a large number of patients(nursing homes or hospitals) have access to web pages that contain datafrom a group of patients using a care-provider interface. Otherinterfaces can also be used with the web site, such as interfaces usedfor: insurance companies, members of a particular company, clinicaltrials for pharmaceutical companies, and e-commerce purposes. Vitalpatient data displayed on these web pages, for example, can be sortedand analyzed depending on the patient's medical history, age, sex,medical condition, and geographic location. The web pages also support awide range of algorithms that can be used to analyze data once they areextracted from the data packets. For example, an instant message oremail can be sent out as an ‘alert’ in response to blood pressureindicating a medical condition that requires immediate attention.Alternatively, the message could be sent out when a data parameter (e.g.systolic blood pressure) exceeds a predetermined value. In some cases,multiple parameters (e.g., fall detection, positioning data, and bloodpressure) can be analyzed simultaneously to generate an alert message.In general, an alert message can be sent out after analyzing one or moredata parameters using any type of algorithm. These algorithms range fromthe relatively simple (e.g., comparing blood pressure to a recommendedvalue) to the complex (e.g., predictive medical diagnoses using ‘datamining’ techniques). In some cases data may be ‘fit’ using algorithmssuch as a linear or non-linear least-squares fitting algorithm.

In one embodiment, a physician, other health care practitioner, oremergency personnel is provided with access to patient medicalinformation through the server 200. In one embodiment, if the wearableappliance detects that the patient needs help, or if the patient decideshelp is needed, the system can call his or her primary care physician.If the patient is unable to access his or her primary care physician (oranother practicing physician providing care to the patient) a call fromthe patient is received, by an answering service or a call centerassociated with the patient or with the practicing physician. The callcenter determines whether the patient is exhibiting symptoms of anemergency condition by polling vital patient information generated bythe wearable device, and if so, the answering service contacts 911emergency service or some other emergency service. The call center canreview falls information, blood pressure information, and other vitalinformation to determine if the patient is in need of emergencyassistance. If it is determined that the patient in not exhibitingsymptoms of an emergent condition, the answering service may thendetermine if the patient is exhibiting symptoms of a non-urgentcondition. If the patient is exhibiting symptoms of a non-urgentcondition, the answering service will inform the patient that he or shemay log into the server 200 for immediate information on treatment ofthe condition. If the answering service determines that the patient isexhibiting symptoms that are not related to a non-urgent condition, theanswering service may refer the patient to an emergency room, a clinic,the practicing physician (when the practicing physician is available)for treatment.

In another embodiment, the wearable appliance permits direct access tothe call center when the user pushes a switch or button on theappliance, for instance. In one implementation, telephones and switchingsystems in call centers are integrated with the home mesh network toprovide for, among other things, better routing of telephone calls,faster delivery of telephone calls and associated information, andimproved service with regard to client satisfaction throughcomputer-telephony integration (CTI). CTI implementations of variousdesign and purpose are implemented both within individual call-centersand, in some cases, at the telephone network level. For example,processors running CTI software applications may be linked to telephoneswitches, service control points (SCPs), and network entry points withina public or private telephone network. At the call-center level,CTI-enhanced processors, data servers, transaction servers, and thelike, are linked to telephone switches and, in some cases, to similarCTI hardware at the network level, often by a dedicated digital link.CTI processors and other hardware within a call-center is commonlyreferred to as customer premises equipment (CPE). It is the CTIprocessor and application software is such centers that providescomputer enhancement to a call center. In a CTI-enhanced call center,telephones at agent stations are connected to a central telephonyswitching apparatus, such as an automatic call distributor (ACD) switchor a private branch exchange (PBX). The agent stations may also beequipped with computer terminals such as personal computer/video displayunit's (PC/VDU's) so that agents manning such stations may have accessto stored data as well as being linked to incoming callers by telephoneequipment. Such stations may be interconnected through the PC/VDUs by alocal area network (LAN). One or more data or transaction servers mayalso be connected to the LAN that interconnects agent stations. The LANis, in turn, typically connected to the CTI processor, which isconnected to the call switching apparatus of the call center.

When a call from a patient arrives at a call center, whether or not thecall has been pre-processed at an SCP, the telephone number of thecalling line and the medical record are made available to the receivingswitch at the call center by the network provider. This service isavailable by most networks as caller-ID information in one of severalformats such as Automatic Number Identification (ANI). Typically thenumber called is also available through a service such as Dialed NumberIdentification Service (DNIS). If the call center is computer-enhanced(CTI), the phone number of the calling party may be used as a key toaccess additional medical and/or historical information from a customerinformation system (CIS) database at a server on the network thatconnects the agent workstations. In this manner information pertinent toa call may be provided to an agent, often as a screen pop on the agent'sPC/VDU.

The call center enables any of a first plurality of physician or healthcare practitioner terminals to be in audio communication over thenetwork with any of a second plurality of patient wearable appliances.The call center will route the call to a physician or other health carepractitioner at a physician or health care practitioner terminal andinformation related to the patient (such as an electronic medicalrecord) will be received at the physician or health care practitionerterminal via the network. The information may be forwarded via acomputer or database in the practicing physician's office or by acomputer or database associated with the practicing physician, a healthcare management system or other health care facility or an insuranceprovider. The physician or health care practitioner is then permitted toassess the patient, to treat the patient accordingly, and to forwardupdated information related to the patient (such as examination,treatment and prescription details related to the patient's visit to thepatient terminal) to the practicing physician via the network 200.

In one embodiment, the system informs a patient of a practicingphysician of the availability of the web services and referring thepatient to the web site upon agreement of the patient. A call from thepatient is received at a call center. The call center enables physiciansto be in audio communication over the network with any patient wearableappliances, and the call is routed to an available physician at one ofthe physician so that the available physician may carry on a two-wayconversation with the patient. The available physician is permitted tomake an assessment of the patient and to treat the patient. The systemcan forward information related to the patient to a health caremanagement system associated with the physician. The health caremanagement system may be a healthcare management organization, a pointof service health care system, or a preferred provider organization. Thehealth care practitioner may be a nurse practitioner or an internist.

The available health care practitioner can make an assessment of thepatient and to conduct an examination of the patient over the network,including optionally by a visual study of the patient. The system canmake an assessment in accordance with a protocol. The assessment can bemade in accordance with a protocol stored in a database and/or making anassessment in accordance with the protocol may include displaying inreal time a relevant segment of the protocol to the available physician.Similarly, permitting the physician to prescribe a treatment may includepermitting the physician to refer the patient to a third party fortreatment and/or referring the patient to a third party for treatmentmay include referring the patient to one or more of a primary carephysician, specialist, hospital, emergency room, ambulance service orclinic. Referring the patient to a third party may additionally includecommunicating with the third party via an electronic link included in arelevant segment of a protocol stored in a protocol database resident ona digital storage medium and the electronic link may be a hypertextlink. When a treatment is being prescribed by a physician, the systemcan communicate a prescription over the network to a pharmacy and/orcommunicating the prescription over the network to the pharmacy mayinclude communicating to the pharmacy instructions to be given to thepatient pertaining to the treatment of the patient. Communicating theprescription over the network to the pharmacy may also includecommunicating the prescription to the pharmacy via a hypertext linkincluded in a relevant segment of a protocol stored in a databaseresident on a digital storage medium. In accordance with another relatedembodiment, permitting the physician to conduct the examination may beaccomplished under conditions such that the examination is conductedwithout medical instruments at the patient terminal where the patient islocated.

In another embodiment, a system for delivering medical examination,diagnosis, and treatment services from a physician to a patient over anetwork includes a first plurality of health care practitioners at aplurality of terminals, each of the first plurality of health carepractitioner terminals including a display device that shows informationcollected by the wearable appliances and a second plurality of patientterminals or wearable appliances in audiovisual communication over anetwork with any of the first plurality of health care practitionerterminals. A call center is in communication with the patient wearableappliances and the health care practitioner terminals, the call centerrouting a call from a patient at one of the patient terminals to anavailable health care practitioner at one of the health carepractitioner terminals, so that the available health care practitionermay carry on a two-way conversation with the patient. A protocoldatabase resident on a digital storage medium is accessible to each ofthe health care practitioner terminals. The protocol database contains aplurality of protocol segments such that a relevant segment of theprotocol may be displayed in real time on the display device of thehealth care practitioner terminal of the available health carepractitioner for use by the available health care practitioner in makingan assessment of the patient. Tthe relevant segment of the protocoldisplayed in real time on the display device of the health carepractitioner terminal may include an electronic link that establishescommunication between the available health care practitioner and a thirdparty and the third party may be one or more of a primary carephysician, specialist, hospital, emergency room, ambulance service,clinic or pharmacy.

In accordance with other related embodiment, the patient wearableappliance may include establish a direct connection to the call centerby pushing a button on the appliance. Further, the protocol database maybe resident on a server that is in communication with each of the healthcare practitioner terminals and each of the health care practitionerterminals may include a local storage device and the protocol databaseis replicated on the local storage device of one or more of thephysician terminals.

In another embodiment, a system for delivering medical examination,diagnosis, and treatment services from a physician to a patient over anetwork includes a first plurality of health care practitionerterminals, each of the first plurality of health care practitionerterminals including a display device and a second plurality of patientterminals in audiovisual communication over a network with any of thefirst plurality of health care practitioner terminals. Each of thesecond plurality of patient terminals includes a camera having pan, tiltand zoom modes, such modes being controlled from the first plurality ofhealth care practitioner terminals. A call center is in communicationwith the patient terminals and the health care practitioner terminalsand the call center routes a call from a patient at one of the patientterminals to an available health care practitioner at one of the healthcare practitioner terminals, so that the available health carepractitioner may carry on a two-way conversation with the patient andvisually observe the patient.

In one embodiment, the information is store in a secure environment,with security levels equal to those of online banking, social securitynumber input, and other confidential information. Conforming to HealthInsurance Portability and Accountability Act (HIPAA) requirements, thesystem creates audit trails, requires logins and passwords, and providesdata encryption to ensure the patient information is private and secure.The HIPAA privacy regulations ensure a national floor of privacyprotections for patients by limiting the ways that health plans,pharmacies, hospitals and other covered entities can use patients'personal medical information. The regulations protect medical recordsand other individually identifiable health information, whether it is onpaper, in computers or communicated orally.

Due to its awareness of the patient's position, the server 200 canoptionally control a mobility assistance device such as a smart cane orrobot. The robotic smart cane sends video from its camera to the server20, which in turn coordinates the position of the robot, as determinedby the cameras 10 mounted in the home as well as the robot camera. Therobot position, as determined by the server 20, is then transmitted tothe robot for navigation. The robot has a frame with an extended handle.The handle includes handle sensors mounted thereon to detect the forceplaces on each handle to receive as input the movement desired by thepatient. In one embodiment, the robot has a control navigation systemthat accepts patient command as well as robot self-guidance command. Themobility is a result of give-and-take between the patient'sself-propulsion and the walker's automated reactions. Thus, when thepatient moves the handle to the right, the robot determines that thepatient is interested in turning and actuates the drive systemsappropriately. However, if the patient is turning into an obstacle, asdetermined by the cameras and the server 20, the drive system providesgentle resistance that tells the patient of an impending collision.

If, for example, a patient does not see a coffee table ahead, the walkerwill detect it, override the patient's steering to avoid it, and therebyprevent a possible fall. Onboard software processes the data from 180degrees of approaching terrain and steers the front wheel towardopenings and away from obstacles.

The control module executes software that enables the robot to movearound its environment safely. The software performs localization,mapping, path planning and obstacle avoidance. In one embodiment, imagesfrom a plurality of wall-mounted cameras 10 are transmitted to theserver 20. The server 20 collects images of the robot and triangulatesthe robot position by cross-referencing the images. The information isthen correlated with the image from the robot-mounted camera and opticalencoders that count the wheel rotations to calculate traveled distancefor range measurement. In this process, a visual map of unique“landmarks” created as the robot moves along its path is annotated withthe robot's position to indicate the position estimate of the landmark.The current image, seen from the robot, is compared with the images inthe database to find matching landmarks. Such matches are used to updatethe position of the robot according to the relative position of thematching landmark. By repeatedly updating the position of landmarksbased on new data, the software incrementally improves the map bycalculating more accurate estimates for the position of the landmarks.An improved map results in more accurate robot position estimates.Better position estimates contribute to better estimates for thelandmark positions and so on. If the environment changes so much thatthe robot no longer recognizes previous landmarks, the robotautomatically updates the map with new landmarks. Outdated landmarksthat are no longer recognized can easily be deleted from the map bysimply determining if they were seen or matched when expected.

Using the obstacle avoidance algorithm, the robot generates correctivemovements to avoid obstacles not represented in the path planner such asopen/closed doors, furniture, people, and more. The robot rapidlydetects obstacles using its sensors and controls its speed and headingto avoid obstacles.

The hazard avoidance mechanisms provide a reflexive response tohazardous situations to insure the robot's safety and guarantee that itdoes not damage itself or the environment. Mechanisms for hazardavoidance include collision detection using not one but a complementaryset of sensors and techniques. For instance, collision avoidance can beprovided using contact sensing, motor load sensing, and vision. Thecombination of multiple sources for collision detection guarantees safecollision avoidance. Collision detection provides a last resort fornegotiating obstacles in case obstacle avoidance fails to do so in thefirst place, which can be caused by moving objects or software andhardware failures.

If the walker is in motion (as determined by the wheel encoder), theforce applied to the brake pads is inversely proportional to thedistance to obstacles. If the walker is stopped, the brakes should befully applied to provide a stable base on which the patient can rest.When the walker is stopped and the patient wishes to move again, thebrakes should come off slowly to prevent the walker from lurchingforward

The walker should mostly follow the patient's commands, as this iscrucial for patient acceptance. For the safety braking and the safetybraking and steering control systems, the control system only influencesthe motion when obstacles or cliffs are near the patient. In otherwords, the walker is, typically, fully patient controlled. For all othersituations, the control system submits to the patient's desire. Thisdoes not mean that the control system shuts down, or does not providethe usual safety features. In fact, all of the control systems fall backon their emergency braking to keep the patient safe. When the controlsystem has had to brake to avoid an obstacle or has given up trying tolead the patient on a particular path, the patient must disengage thebrakes (via a pushbutton) or re-engage the path following (again via apushbutton) to regain control or allow collaboration again. This letsthe patient select the walker's mode manually when they disagree withthe control system's choices.

FIG. 5 shows an exemplary process to monitor patient. First, the processsets up mesh network appliances (1000). Next, the process determinespatient position using in-door positioning system (1002). The processthen determines patient movement using accelerometer output (1004).Sharp accelerations may be used to indicate fall. Further, the z axisaccelerometer changes can indicate the height of the appliance from thefloor and if the height is near zero, the system infers that the patienthad fallen. The system can also determine vital parameter includingpatient heart rate (1006). The system determines if patient needsassistance based on in-door position, fall detection and vital parameter(1008). If a fall is suspected, the system confirms the fall bycommunicating with the patient prior to calling a third party such asthe patient's physician, nurse, family member, 911, 511, 411, or a paidcall center to get assistance for the patient (1010). If confirmed or ifthe patient is non-responsive, the system contacts the third party andsends voice over mesh network to appliance on the patient to allow oneor more third parties to talk with the patient (1012). If needed, thesystem calls and/or conferences emergency personnel into the call(1014).

In one embodiment, if the patient is outside of the mesh network rangesuch as when the user is traveling away from his/her home, the systemcontinuously records information into memory until the home mesh networkis reached or until the monitoring appliance reaches an internet accesspoint. While the wearable appliance is outside of the mesh networkrange, the device searches for a cell phone with an expansion cardplugged into a cell phone expansion slot such as the SDIO slot. If thewearable appliance detects a cell phone that is mesh network compatible,the wearable appliance communicates with the cell phone and providesinformation to the server 200 using the cellular connection. In oneembodiment, a Zigbee SDIO card from C-guys, Inc., enablesdevice-to-device communications for PDAs and smart phones. C-guys'ZigBee SDIO card includes the company's CG-100 SDIO applicationinterface controller, which is designed to convert an application signalto an SD signal (or vice versa). The ZigBee card can provide signalranges of up to 10 m in the 2.4 GHz band and data rates of up to 200kbps. The card has peer-to-peer communications mode and supports directapplication to PDAs or any SD supported hand-held cell phones. In thisembodiment, the PDA or cell phone can provide a GPS position informationinstead of the indoor position information generated by the mesh networkappliances 8. The cell phone GPS position information, accelerometerinformation and vital information such as heart rate information istransmitted using the cellular channel to the server 200 for processingas is normal. In another embodiment where the phone works through WiFi(802.11) or WiMAX (802.16) or ultra-wideband protocol instead of thecellular protocol, the wearable appliance can communicate over theseprotocols using a suitable mesh network interface to the phone. Ininstances where the wearable appliance is outside of its home base and adangerous condition such as a fall is detected, the wearable appliancecan initiate a distress call to the authorized third party usingcellular, WiFi, WiMAX, or UWB protocols as is available.

FIG. 6A shows a portable embodiment of the present invention where thevoice recognizer is housed in a wrist-watch. As shown in FIG. 6, thedevice includes a wrist-watch sized case 1380 supported on a wrist band1374. The case 1380 may be of a number of variations of shape but can beconveniently made a rectangular, approaching a box-like configuration.The wrist-band 1374 can be an expansion band or a wristwatch strap ofplastic, leather or woven material. The processor or CPU of the wearableappliance is connected to a radio frequency (RF) transmitter/receiver(such as a Bluetooth device, a Zigbee device, a WiFi device, a WiMAXdevice, or an 802.X transceiver, among others.

In one embodiment, the back of the device is a conductive metalelectrode 1381 that in conjunction with a second electrode 1383 mountedon the wrist band 1374, enables differential EKG or ECG to be measured.The electrical signal derived from the electrodes is typically lmVpeak-peak. In one embodiment where only one electrode 1381 or 1383 isavailable, an amplification of about 1000 is necessary to render thissignal usable for heart rate detection. In the embodiment withelectrodes 1381 and 1383 available, a differential amplifier is used totake advantage of the identical common mode signals from the EKG contactpoints, the common mode noise is automatically cancelled out using amatched differential amplifier. In one embodiment, the differentialamplifier is a Texas Instruments INA321 instrumentation amplifier thathas matched and balanced integrated gain resistors. This device isspecified to operate with a minimum of 2.7V single rail power supply.The INA321 provides a fixed amplification of 5× for the EKG signal. Withits CMRR specification of 94 dB extended up to 3 KHz the INA321 rejectsthe common mode noise signals including the line frequency and itsharmonics. The quiescent current of the INA321 is 40 mA and the shutdown mode current is less than 1 mA. The amplified EKG signal isinternally fed to the on chip analog to digital converter. The ADCsamples the EKG signal with a sampling frequency of 512 Hz. Precisesampling period is achieved by triggering the ADC conversions with atimer that is clocked from a 32.768 kHz low frequency crystaloscillator. The sampled EKG waveform contains some amount of superimposed line frequency content. This line frequency noise is removed bydigitally filtering the samples. In one implementation, a 17-tap lowpass FIR filter with pass band upper frequency of 6 Hz and stop bandlower frequency of 30 Hz is implemented in this application. The filtercoefficients are scaled to compensate the filter attenuation and provideadditional gain for the EKG signal at the filter output. This adds up toa total amplification factor of greater than 1000× for the EKG signal.

The wrist band 1374 can also contain other electrical devices such asultrasound transducer, optical transducer or electromagnetic sensors,among others. In one embodiment, the transducer is an ultrasonictransducer that generates and transmits an acoustic wave upon commandfrom the CPU during one period and listens to the echo returns during asubsequent period. In use, the transmitted bursts of sonic energy arescattered by red blood cells flowing through the subject's radialartery, and a portion of the scattered energy is directed back towardthe ultrasonic transducer 84. The time required for the return energy toreach the ultrasonic transducer varies according to the speed of soundin the tissue and according to the depth of the artery. Typical transittimes are in the range of 6 to 7 microseconds. The ultrasonic transduceris used to receive the reflected ultrasound energy during the dead timesbetween the successive transmitted bursts. The frequency of theultrasonic transducer's transmit signal will differ from that of thereturn signal, because the scattering red blood cells within the radialartery are moving. Thus, the return signal, effectively, is frequencymodulated by the blood flow velocity.

A driving and receiving circuit generates electrical pulses which, whenapplied to the transducer, produce acoustic energy having a frequency onthe order of 8 MHz, a pulse width or duration of approximately 8microseconds, and a pulse repetition interval (PRI) of approximately 16μs, although other values of frequency, pulse width, and PRI may beused. In one embodiment, the transducer 84 emits an 8 microsecond pulse,which is followed by an 8 microsecond “listen” period, every 16microseconds. The echoes from these pulses are received by theultrasonic transducer 84 during the listen period. The ultrasonictransducer can be a ceramic piezoelectric device of the type well knownin the art, although other types may be substituted.

An analog signal representative of the Doppler frequency of the echo isreceived by the transducer and converted to a digital representation bythe ADC, and supplied to the CPU for signal processing. Within the CPU,the digitized Doppler frequency is scaled to compute the blood flowvelocity within the artery based on the Doppler frequency. Based on thereal time the blood flow velocity, the CPU applies the vital model tothe corresponding blood flow velocity to produce the estimated bloodpressure value.

Prior to operation, calibration is done using a calibration device andthe monitoring device to simultaneously collect blood pressure values(systolic, diastolic pressures) and a corresponding blood flow velocitygenerated by the monitoring device. The calibration device is attachedto the base station and measures systolic and diastolic blood pressureusing a cuff-based blood pressure monitoring device that includes amotor-controlled pump and data-processing electronics. While thecuff-based blood pressure monitoring device collects patient data, thetransducer collects patient data in parallel and through the watch'sradio transmitter, blood flow velocity is sent to the base station forgenerating a computer model that converts the blood flow velocityinformation into systolic and diastolic blood pressure values and thisinformation is sent wirelessly from the base station to the watch fordisplay and to a remote server if needed. This process is repeated at alater time (e.g., 15 minutes later) to collect a second set ofcalibration parameters. In one embodiment, the computer model fits theblood flow velocity to the systolic/diastolic values. In anotherembodiment, the computer trains a neural network or HMM to recognize thesystolic and diastolic blood pressure values.

After the computer model has been generated, the system is ready forreal-time blood pressure monitoring. In an acoustic embodiment, thetransducer directs ultrasound at the patient's artery and subsequentlylistens to the echos therefrom. The echoes are used to determine bloodflow, which is fed to the computer model to generate the systolic anddiastolic pressure values as well as heart rate value. The CPU's outputsignal is then converted to a form useful to the user such as a digitalor analog display, computer data file, or audible indicator. The outputsignal can drive a speaker to enable an operator to hear arepresentation of the Doppler signals and thereby to determine when thetransducer is located approximately over the radial artery. The outputsignal can also be wirelessly sent to a base station for subsequentanalysis by a physician, nurse, caregiver, or treating professional. Theoutput signal can also be analyzed for medical attention and medicaltreatment.

It is noted that while the above embodiment utilizes a preselected pulseduration of 8 microseconds and pulse repetition interval of 16microseconds, other acoustic sampling techniques may be used inconjunction with the invention. For example, in a second embodiment ofthe ultrasonic driver and receiver circuit (not shown), the acousticpulses are range-gated with a more complex implementation of the gatelogic. As is well known in the signal processing arts, range-gating is atechnique by which the pulse-to-pulse interval is varied based on thereceipt of range information from earlier emitted and reflected pulses.Using this technique, the system may be “tuned” to receive echoesfalling within a specific temporal window which is chosen based on therange of the echo-producing entity in relation to the acoustic source.The delay time before the gate is turned on determines the depth of thesample volume. The amount of time the gate is activated establishes theaxial length of the sample volume. Thus, as the acoustic source (in thiscase the ultrasonic transducer 84) is tuned to the echo-producing entity(red blood cells, or arterial walls), the pulse repetition interval isshortened such that the system may obtain more samples per unit time,thereby increasing its resolution. It will be recognized that otheracoustic processing techniques may also be used, all of which areconsidered to be equivalent.

In one optical embodiment, the transducer can be an optical transducer.The optical transducer can be a light source and a photo-detectorembedded in the wrist band portions 1374. The light source can belight-emitting diodes that generate red (λ^(˜)630 nm) and infrared(λ^(˜)900 nm) radiation, for example. The light source and thephoto-detector are slidably adjustable and can be moved along the wristband to optimize beam transmission and pick up. As the heart pumps bloodthrough the patient's finger, blood cells absorb and transmit varyingamounts of the red and infrared radiation depending on how much oxygenbinds to the cells' hemoglobin. The photo-detector detects transmissionat the predetermined wavelengths, for example red and infraredwavelengths, and provides the detected transmission to a pulse-oximetrycircuit embedded within the wrist-watch. The output of thepulse-oximetry circuit is digitized into a time-dependent opticalwaveform, which is then sent back to the pulse-oximetry circuit andanalyzed to determine the user's vital signs.

In the electromagnetic sensor embodiment, the wrist band 1374 is aflexible plastic material incorporated with a flexible magnet. Themagnet provides a magnetic field, and one or more electrodes similar toelectrode 1383 are positioned on the wrist band to measure voltage dropswhich are proportional to the blood velocity. The electromagneticembodiment may be mounted on the upper arm of the patient, on the ankleor on the neck where peripheral blood vessels pass through and theirblood velocity may be measured with minimal interruptions. The flexiblemagnet produces a pseudo-uniform (non-gradient) magnetic field. Themagnetic field can be normal to the blood flow direction when wrist band1374 is mounted on the user's wrist or may be a rotative pseudo-uniformmagnetic field so that the magnetic field is in a transversal directionin respect to the blood flow direction. The electrode output signals areprocessed to obtain a differential measurement enhancing the signal tonoise ratio. The flow information is derived based on the periodicity ofthe signals. The decoded signal is filtered over several periods andthen analyzed for changes used to estimate artery and vein blood flow.Systemic stroke volume and cardiac output may be calculated from theperipheral SV index value.

The wrist-band 1374 further contains an antenna 1376 for transmitting orreceiving radio frequency signals. The wristband 1374 and the antenna1376 inside the band are mechanically coupled to the top and bottomsides of the wrist-watch housing 1380. Further, the antenna 1376 iselectrically coupled to a radio frequency transmitter and receiver forwireless communications with another computer or another user. Althougha wrist-band is disclosed, a number of substitutes may be used,including a belt, a ring holder, a brace, or a bracelet, among othersuitable substitutes known to one skilled in the art. The housing 1380contains the processor and associated peripherals to provide thehuman-machine interface. A display 1382 is located on the front sectionof the housing 1380. A speaker 1384, a microphone 1388, and a pluralityof push-button switches 1386 and 1390 are also located on the frontsection of housing 1380.

The electronic circuitry housed in the watch case 1380 detects adverseconditions such as falls or seizures. In one implementation, thecircuitry can recognize speech, namely utterances of spoken words by theuser, and converting the utterances into digital signals. The circuitryfor detecting and processing speech to be sent from the wristwatch tothe base station 20 over the mesh network includes a central processingunit (CPU) connected to a ROM/RAM memory via a bus. The CPU is apreferably low power 16-bit or 32-bit microprocessor and the memory ispreferably a high density, low-power RAM. The CPU is coupled via the busto processor wake-up logic, one or more accelerometers to detect suddenmovement in a patient, an ADC 102 which receives speech input from themicrophone. The ADC converts the analog signal produced by themicrophone into a sequence of digital values representing the amplitudeof the signal produced by the microphone at a sequence of evenly spacedtimes. The CPU is also coupled to a digital to analog (D/A) converter,which drives the speaker to communicate with the user. Speech signalsfrom the microphone are first amplified, pass through an antialiasingfilter before being sampled. The front-end processing includes anamplifier, a bandpass filter to avoid antialiasing, and ananalog-to-digital (A/D) converter or a CODEC. To minimize space, theADC, the DAC and the interface for wireless transceiver and switches maybe integrated into one integrated circuit to save space. In oneembodiment, the wrist watch acts as a walkie-talkie so that voice isreceived over the mesh network by the base station 20 and then deliveredto a call center over the POTS or PSTN network. In another embodiment,voice is provided to the call center using the Internet through suitableVOIP techniques. In one embodiment, speech recognition such as a speechrecognizer is discussed in U.S. Pat. No. 6,070,140 by the inventor ofthe instant invention, the content of which is incorporated byreference.

In one embodiment, the wireless nodes convert freely available energyinherent in most operating environments into conditioned electricalpower. Energy harvesting is defined as the conversion of ambient energyinto usable electrical energy. When compared with the energy stored incommon storage elements, like batteries and the like, the environmentrepresents a relatively inexhaustible source of energy. Energyharvesters can be based on piezoelectric devices, solar cells orelectromagnetic devices that convert mechanical vibrations.

Power generation with piezoelectrics can be done with body vibrations orby physical compression (impacting the material and using a rapiddeceleration using foot action, for example). The vibration energyharvester consists of three main parts. A piezoelectric transducer (PZT)serves as the energy conversion device, a specialized power converterrectifies the resulting voltage, and a capacitor or battery stores thepower. The PZT takes the form of an aluminum cantilever with apiezoelectric patch. The vibration-induced strain in the PZT produces anac voltage. The system repeatedly charges a battery or capacitor, whichthen operates the EKG/EMG sensors or other sensors at a relatively lowduty cycle. In one embodiment, a vest made of piezoelectric materialscan be wrapped around a person's chest to generate power when strainedthrough breathing as breathing increases the circumference of the chestfor an average human by about 2.5 to 5 cm. Energy can be constantlyharvested because breathing is a constant activity, even when a personis sedate. In another embodiment, piezoelectric materials are placed inbetween the sole and the insole; therefore as the shoe bends fromwalking, the materials bend along with it. When the stave is bent, thepiezoelectric sheets on the outside surface are pulled into expansion,while those on the inside surface are pushed into contraction due totheir differing radii of curvature, producing voltages across theelectrodes. In another embodiment, PZT materials from AdvancedCerametrics, Inc., Lambertville, N.J. can be incorporated into flexible,motion sensitive (vibration, compression or flexure), active fibercomposite shapes that can be placed in shoes, boots, and clothing or anylocation where there is a source of waste energy or mechanical force.These flexible composites generate power from the scavenged energy andharness it using microprocessor controls developed specifically for thispurpose. Advanced Cerametric's viscose suspension spinning process(VSSP) can produce fibers ranging in diameter from 10 μm ( 1/50 of ahuman hair) to 250 μm and mechanical to electrical transductionefficiency can reach 70 percent compared with the 16-18 percent commonto solar energy conversion. The composite fibers can be molded intouser-defined shapes and is flexible and motion-sensitive. In oneimplementation, energy is harvested by the body motion such as the footaction or vibration of the PZT composites. The energy is converted andstored in a low-leakage charge circuit until a predetermined thresholdvoltage is reached. Once the threshold is reached, the regulated poweris allowed to flow for a sufficient period to power the wireless nodesuch as the Zigbee CPU/transceiver. The transmission is detected bynearby wireless nodes that are AC-powered and forwarded to the basestation for signal processing. Power comes from the vibration of thesystem being monitored and the unit requires no maintenance, thusreducing life-cycle costs. In one embodiment, the housing of the unitcan be PZT composite, thus reducing the weight.

In another embodiment, body energy generation systems include electroactive polymers (EAPs) and dielectric elastomers. EAPs are a class ofactive materials that have a mechanical response to electricalstimulation and produce an electric potential in response to mechanicalstimulation. EAPs are divided into two categories, electronic, driven byelectric field, and ionic, driven by diffusion of ions. In oneembodiment, ionic polymers are used as biological actuators that assistmuscles for organs such as the heart and eyes. Since the ionic polymersrequire a solvent, the hydrated human body provides a naturalenvironment. Polymers are actuated to contract, assisting the heart topump, or correcting the shape of the eye to improve vision. Another useis as miniature surgical tools that can be inserted inside the body.EAPs can also be used as artificial smooth muscles, one of the originalideas for EAPs. These muscles could be placed in exoskeletal suits forsoldiers or prosthetic devices for disabled persons. Along with theenergy generation device, ionic polymers can be the energy storagevessel for harvesting energy. The capacitive characteristics of the EAPallow the polymers to be used in place of a standard capacitor bank.With EAP based jacket, when a person moves his/her arms, it will put theelectro active material around the elbow in tension to generate power.Dielectric elastomers can support 50-100% area strain and generate powerwhen compressed. Although the material could again be used in a bendingarm type application, a shoe type electric generator can be deployed byplacing the dielectric elastomers in the sole of a shoe. The constantcompressive force provided by the feet while walking would ensureadequate power generation.

For wireless nodes that require more power, electromagnetics, includingcoils, magnets, and a resonant beam, and micro-generators can be used toproduce electricity from readily available foot movement. Typically, atransmitter needs about 30 mW, but the device transmits for only tens ofmilliseconds, and a capacitor in the circuit can be charged usingharvested energy and the capacitor energy drives the wirelesstransmission, which is the heaviest power requirement. Electromagneticenergy harvesting uses a magnetic field to convert mechanical energy toelectrical. A coil attached to the oscillating mass traverses through amagnetic field that is established by a stationary magnet. The coiltravels through a varying amount of magnetic flux, inducing a voltageaccording to Faraday's law. The induced voltage is inherently small andmust therefore be increased to viably source energy. Methods to increasethe induced voltage include using a transformer, increasing the numberof turns of the coil, and/or increasing the permanent magnetic field.Electromagnetic devices use the motion of a magnet relative to a wirecoil to generate an electric voltage. A permanent magnet is placedinside a wound coil. As the magnet is moved through the coil it causes achanging magnetic flux. This flux is responsible for generating thevoltage which collects on the coil terminals. This voltage can then besupplied to an electrical load. Because an electromagnetic device needsa magnet to be sliding through the coil to produce voltage, energyharvesting through vibrations is an ideal application. In oneembodiment, electromagnetic devices are placed inside the heel of ashoe. One implementation uses a sliding magnet-coil design, the other,opposing magnets with one fixed and one free to move inside the coil. Ifthe length of the coil is increased, which increases the turns, thedevice is able to produce more power.

In an electrostatic (capacitive) embodiment, energy harvesting relies onthe changing capacitance of vibration-dependant varactors. A varactor,or variable capacitor, is initially charged and, as its plates separatebecause of vibrations, mechanical energy is transformed into electricalenergy. MEMS variable capacitors are fabricated through relativelymature silicon micro-machining techniques.

In another embodiment, the wireless node can be powered from thermaland/or kinetic energy. Temperature differentials between oppositesegments of a conducting material result in heat flow and consequentlycharge flow, since mobile, high-energy carriers diffuse from high to lowconcentration regions. Thermopiles consisting of n- and p-type materialselectrically joined at the high-temperature junction are thereforeconstructed, allowing heat flow to carry the dominant charge carriers ofeach material to the low temperature end, establishing in the process avoltage difference across the base electrodes. The generated voltage andpower is proportional to the temperature differential and the Seebeckcoefficient of the thermoelectric materials. Body heat from a user'swrist is captured by a thermoelectric element whose output is boostedand used to charge the a lithium ion rechargeable battery. The unitutilizes the Seeback Effect which describes the voltage created when atemperature difference exists across two different metals. Thethermoelectric generator takes body heat and dissipates it to theambient air, creating electricity in the process.

In another embodiment, the kinetic energy of a person's movement isconverted into energy. As a person moves their weight, a small weightinside the wireless node moves like a pendulum and turns a magnet toproduce electricity which can be stored in a super-capacitor or arechargeable lithium battery. Similarly, in a vibration energyembodiment, energy extraction from vibrations is based on the movementof a “spring-mounted” mass relative to its support frame. Mechanicalacceleration is produced by vibrations that in turn cause the masscomponent to move and oscillate (kinetic energy). This relativedisplacement causes opposing frictional and damping forces to be exertedagainst the mass, thereby reducing and eventually extinguishing theoscillations. The damping forces literally absorb the kinetic energy ofthe initial vibration. This energy can be converted into electricalenergy via an electric field (electrostatic), magnetic field(electromagnetic), or strain on a piezoelectric material.

Another embodiment extracts energy from the surrounding environmentusing a small rectenna (microwave-power receivers or ultrasound powerreceivers) placed in patches or membranes on the skin or alternativelyinjected underneath the skin.

The rectanna converts the received emitted power back to usable lowfrequency/dc power. A basic rectanna consists of an antenna, a low passfilter, an ac/dc converter and a dc bypass filter. The rectanna cancapture renewable electromagnetic energy available in the radiofrequency (RF) bands such as AM radio, FM radio, TV, very high frequency(VHF), ultra high frequency (UHF), global system for mobilecommunications (GSM), digital cellular systems (DCS) and especially thepersonal communication system (PCS) bands, and unlicensed ISM bands suchas 2.4 GHz and 5.8 GHz bands, among others. The system captures theubiquitous electromagnetic energy (ambient RF noise and signals)opportunistically present in the environment and transforming thatenergy into useful electrical power. The energy-harvesting antenna ispreferably designed to be a wideband, omnidirectional antenna or antennaarray that has maximum efficiency at selected bands of frequenciescontaining the highest energy levels. In a system with an array ofantennas, each antenna in the array can be designed to have maximumefficiency at the same or different bands of frequency from one another.The collected RF energy is then converted into usable DC power using adiode-type or other suitable rectifier. This power may be used to drive,for example, an amplifier/filter module connected to a second antennasystem that is optimized for a particular frequency and application. Oneantenna system can act as an energy harvester while the other antennaacts as a signal transmitter/receiver. The antenna circuit elements areformed using standard wafer manufacturing techniques. The antenna outputis stepped up and rectified before presented to a trickle charger. Thecharger can recharge a complete battery by providing a larger potentialdifference between terminals and more power for charging during a periodof time. If battery includes individual micro-battery cells, the tricklecharger provides smaller amounts of power to each individual batterycell, with the charging proceeding on a cell by cell basis. Charging ofthe battery cells continues whenever ambient power is available. As theload depletes cells, depleted cells are switched out with charged cells.The rotation of depleted cells and charged cells continues as required.Energy is banked and managed on a micro-cell basis.

In a solar cell embodiment, photovoltaic cells convert incident lightinto electrical energy. Each cell consists of a reverse biased pn+junction, where light interfaces with the heavily doped and narrow n+region. Photons are absorbed within the depletion region, generatingelectron-hole pairs. The built-in electric field of the junctionimmediately separates each pair, accumulating electrons and holes in then+ and p− regions, respectively, and establishing in the process an opencircuit voltage. With a load connected, accumulated electrons travelthrough the load and recombine with holes at the p-side, generating aphotocurrent that is directly proportional to light intensity andindependent of cell voltage.

As the energy-harvesting sources supply energy in irregular, random“bursts,” an intermittent charger waits until sufficient energy isaccumulated in a specially designed transitional storage such as acapacitor before attempting to transfer it to the storage device,lithium-ion battery, in this case. Moreover, the system must partitionits functions into time slices (time-division multiplex), ensuringenough energy is harvested and stored in the battery before engaging inpower-sensitive tasks. Energy can be stored using a secondary(rechargeable) battery and/or a supercapacitor. The differentcharacteristics of batteries and supercapacitors make them suitable fordifferent functions of energy storage. Supercapacitors provide the mostvolumetrically efficient approach to meeting high power pulsed loads. Ifthe energy must be stored for a long time, and released slowly, forexample as back up, a battery would be the preferred energy storagedevice. If the energy must be delivered quickly, as in a pulse for RFcommunications, but long term storage is not critical, a supercapacitorwould be sufficient. The system can employ i) a battery (or severalbatteries), ii) a supercapacitor (or supercapacitors), or iii) acombination of batteries and supercapacitors appropriate for theapplication of interest. In one embodiment, a microbattery and amicrosupercapacitor can be used to store energy. Like batteries,supercapacitors are electrochemical devices; however, rather thangenerating a voltage from a chemical reaction, supercapacitors storeenergy by separating charged species in an electrolyte. In oneembodiment, a flexible, thin-film, rechargeable battery from CymbetCorp. of Elk River, Minn. provides 3.6V and can be recharged by areader. The battery cells can be from 5 to 25 microns thick. Thebatteries can be recharged with solar energy, or can be recharged byinductive coupling. The tag is put within range of a coil attached to anenergy source. The coil “couples” with the antenna on the RFID tag,enabling the tag to draw energy from the magnetic field created by thetwo coils.

FIG. 6B shows an exemplary mesh network working with the wearableappliance of FIG. 6A. Data collected and communicated on the display1382 of the watch as well as voice is transmitted to a base station 1390for communicating over a network to an authorized party 1394. The watchand the base station is part of a mesh network that may communicate witha medicine cabinet to detect opening or to each medicine container 1391to detect medication compliance. Other devices include mesh networkthermometers, scales, or exercise devices. The mesh network alsoincludes a plurality of home/room appliances 1392-1399. The ability totransmit voice is useful in the case the patient has fallen down andcannot walk to the base station 1390 to request help. Hence, in oneembodiment, the watch captures voice from the user and transmits thevoice over the Zigbee mesh network to the base station 1390. The basestation 1390 in turn dials out to an authorized third party to allowvoice communication and at the same time transmits the collected patientvital parameter data and identifying information so that help can bedispatched quickly, efficiently and error-free. In one embodiment, thebase station 1390 is a POTS telephone base station connected to thewired phone network. In a second embodiment, the base station 1390 canbe a cellular telephone connected to a cellular network for voice anddata transmission. In a third embodiment, the base station 1390 can be aWiMAX or 802.16 standard base station that can communicate VOIP and dataover a wide area network. I one implementation, Zigbee or 802.15appliances communicate locally and then transmits to the wide areanetwork (WAN) such as the Internet over WiFi or WiMAX. Alternatively,the base station can communicate with the WAN over POTS and a wirelessnetwork such as cellular or WiMAX or both.

One embodiment of FIG. 6B includes bioelectrical impedance (BI)spectroscopy sensors in addition to or as alternates to EKG sensors andheart sound transducer sensors. BI spectroscopy is based on Ohm's Law:current in a circuit is directly proportional to voltage and inverselyproportional to resistance in a DC circuit or impedance in analternating current (AC) circuit. Bioelectric impedance exchangeselectrical energy with the patient body or body segment. The exchangedelectrical energy can include alternating current and/or voltage anddirect current and/or voltage. The exchanged electrical energy caninclude alternating currents and/or voltages at one or more frequencies.For example, the alternating currents and/or voltages can be provided atone or more frequencies between 100 Hz and 1 MHz, preferably at one ormore frequencies between 5 KHz and 250 KHz. A BI instrument operating atthe single frequency of 50 KHz reflects primarily the extra cellularwater compartment as a very small current passes through the cell.Because low frequency (<1 KHz) current does not penetrate the cells andthat complete penetration occurs only at a very high frequency (>1 MHz),multi-frequency BI or bioelectrical impedance spectroscopy devices canbe used to scan a wide range of frequencies.

In a tetrapolar implementation, two electrodes on the wrist watch orwrist band are used to apply AC or DC constant current into the body orbody segment. The voltage signal from the surface of the body ismeasured in terms of impedance using the same or an additional twoelectrodes on the watch or wrist band. In a bipolar implementation, oneelectrode on the wrist watch or wrist band is used to apply AC or DCconstant current into the body or body segment. The voltage signal fromthe surface of the body is measured in terms of impedance using the sameor an alternative electrode on the watch or wrist band. The system ofFIG. 6B may include a BI patch 1400 that wirelessly communicates BIinformation with the wrist watch. Other patches 1400 can be used tocollect other medical information or vital parameter and communicatewith the wrist watch or base station or the information could be relayedthrough each wireless node or appliance to reach a destination appliancesuch as the base station, for example. The system of FIG. 6B can alsoinclude a head-cap 1402 that allows a number of EEG probes access to thebrain electrical activities, EKG probes to measure cranial EKG activity,as well as BI probes to determine cranial fluid presence indicative of astroke. As will be discussed below, the EEG probes allow the system todetermine cognitive status of the patient to determine whether a strokehad just occurred, the EKG and the BI probes provide information on thestroke to enable timely treatment to minimize loss of functionality tothe patient if treatment is delayed.

Bipolar or tetrapolar electrode systems can be used in the BIinstruments. Of these, the tetrapolar system provides a uniform currentdensity distribution in the body segment and measures impedance withless electrode interface artifact and impedance errors. In thetetrapolar system, a pair of surface electrodes (I1, I2) is used ascurrent electrodes to introduce a low intensity constant current at highfrequency into the body. A pair of electrodes (E1, E2) measures changesaccompanying physiological events. Voltage measured across E1-E2 isdirectly proportional to the segment electrical impedance of the humansubject. Circular flat electrodes as well as band type electrodes can beused. In one embodiment, the electrodes are in direct contact with theskin surface. In other embodiments, the voltage measurements may employone or more contactless, voltage sensitive electrodes such asinductively or capacitively coupled electrodes. The current applicationand the voltage measurement electrodess in these embodiments can be thesame, adjacent to one another, or at significantly different locations.The electrode(s) can apply current levels from 20 uA to 10 mA rms at afrequency range of 20-100 KHz. A constant current source and high inputimpedance circuit is used in conjunction with the tetrapolar electrodeconfiguration to avoid the contact pressure effects at theelectrode-skin interface.

The BI sensor can be a Series Model which assumes that there is oneconductive path and that the body consists of a series of resistors. Anelectrical current, injected at a single frequency, is used to measurewhole body impedance (i.e., wrist to ankle) for the purpose ofestimating total body water and fat free mass. Alternatively, the BIinstrument can be a Parallel BI Model In this model of impedance, theresistors and capacitors are oriented both in series and in parallel inthe human body. Whole body BI can be used to estimate TBW and FFM inhealthy subjects or to estimate intracellular water (ICW) and body cellmass (BCM). High-low BI can be used to estimate extracellular water(ECW) and total body water (TBW). Multi-frequency BI can be used toestimate ECW, ICW, and TBW; to monitor changes in the ECW/BCM andECW/TBW ratios in clinical populations. The instrument can also be aSegmental BI Model and can be used in the evaluation of regional fluidchanges and in monitoring extra cellular water in patients with abnormalfluid distribution, such as those undergoing hemodialysis. Segmental BIcan be used to measure fluid distribution or regional fluid accumulationin clinical populations. Upper-body and Lower-body BI can be used toestimate percentage BF in healthy subjects with normal hydration statusand fluid distribution. The BI sensor can be used to detect acutedehydration, pulmonary edema (caused by mitral stenosis or leftventricular failure or congestive heart failure, among others), orhyperhydration cause by kidney dialysis, for example. In one embodiment,the system determines the impedance of skin and subcutaneous adiposetissue using tetrapolar and bipolar impedance measurements. In thebipolar arrangement the inner electrodes act both as the electrodes thatsend the current (outer electrodes in the tetrapolar arrangement) and asreceiving electrodes. If the outer two electrodes (electrodes sendingcurrent) are superimposed onto the inner electrodes (receivingelectrodes) then a bipolar BIA arrangement exists with the sameelectrodes acting as receiving and sending electrodes. The difference inimpedance measurements between the tetrapolar and bipolar arrangementreflects the impedance of skin and subcutaneous fat. The differencebetween the two impedance measurements represents the combined impedanceof skin and subcutaneous tissue at one or more sites. The systemdetermines the resistivities of skin and subcutaneous adipose tissue,and then calculates the skinfold thickness (mainly due to adiposetissue).

Various BI analysis methods can be used in a variety of clinicalapplications such as to estimate body composition, to determine totalbody water, to assess compartmentalization of body fluids, to providecardiac monitoring, measure blood flow, dehydration, blood loss, woundmonitoring, ulcer detection and deep vein thrombosis. Other uses for theBI sensor includes detecting and/or monitoring hypovolemia, hemorrhageor blood loss. The impedance measurements can be made sequentially overa period of in time; and the system can determine whether the subject isexternally or internally bleeding based on a change in measuredimpedance. The watch can also report temperature, heat flux,vasodilation and blood pressure along with the BI information.

In one embodiment, the BI system monitors cardiac function usingimpedance cardiography (ICG) technique. ICG provides a single impedancetracing, from which parameters related to the pump function of theheart, such as cardiac output (CO), are estimated. ICG measures thebeat-to-beat changes of thoracic bioimpedance via four dual sensorsapplied on the neck and thorax in order to calculate stroke volume (SV).By using the resistivity p of blood and the length L of the chest, theimpedance change ΔZ and base impedance (Zo) to the volume change ΔV ofthe tissue under measurement can be derived as follows:

${\Delta\; V} = {\rho\frac{L^{2}}{Z_{0}^{2}}\Delta\; Z}$

In one embodiment, SV is determined as a function of the firstderivative of the impedance waveform (dZ/dtmax) and the left ventricularejection time (LVET)

${SV} = {\rho\frac{L^{2}}{Z_{0}^{2}}\left( \frac{\mathbb{d}Z}{\mathbb{d}t} \right)_{\max}{LVET}}$

In one embodiment, L is approximated to be 17% of the patient's height(H) to yield the following:

${SV} = {\left( \frac{\left( {0.17H} \right)^{3}}{4.2} \right)\frac{\left( \frac{\mathbb{d}Z}{\mathbb{d}t} \right)_{\max}}{Z_{0}}\mspace{14mu}{LVET}}$

In another embodiment, δ or the actual weight divided by the idealweight is used:

${SV} = {\delta \times \left( \frac{\left( {0.17H} \right)^{3}}{4.2} \right)\frac{\left( \frac{\mathbb{d}Z}{\mathbb{d}t} \right)_{\max}}{Z_{0}}\mspace{14mu}{LVET}}$

The impedance cardiographic embodiment allows hemodynamic assessment tobe regularly monitored to avoid the occurrence of an acute cardiacepisode. The system provides an accurate, noninvasive measurement ofcardiac output (CO) monitoring so that ill and surgical patientsundergoing major operations such as coronary artery bypass graft (CABG)would benefit. In addition, many patients with chronic and comorbiddiseases that ultimately lead to the need for major operations and othercostly interventions might benefit from more routine monitoring of COand its dependent parameters such as systemic vascular resistance (SVR).

Once SV has been determined, CO can be determined according to thefollowing expression:CO=SV*HR, where HR=heart rate

CO can be determined for every heart-beat. Thus, the system candetermine SV and CO on a beat-to-beat basis.

In one embodiment to monitor heart failure, an array of BI sensors areplace in proximity to the heart. The array of BI sensors detect thepresence or absence, or rate of change, or body fluids proximal to theheart. The BI sensors can be supplemented by the EKG sensors. A normal,healthy, heart beats at a regular rate. Irregular heart beats, known ascardiac arrhythmia, on the other hand, may characterize an unhealthycondition. Another unhealthy condition is known as congestive heartfailure (“CHF”). CHF, also known as heart failure, is a condition wherethe heart has inadequate capacity to pump sufficient blood to meetmetabolic demand. CHF may be caused by a variety of sources, including,coronary artery disease, myocardial infarction, high blood pressure,heart valve disease, cardiomyopathy, congenital heart disease,endocarditis, myocarditis, and others. Unhealthy heart conditions may betreated using a cardiac rhythm management (CRM) system. Examples of CRMsystems, or pulse generator systems, include defibrillators (includingimplantable cardioverter defibrillator), pacemakers and other cardiacresynchronization devices.

In one implementation, BIA measurements can be made using an array ofbipolar or tetrapolar electrodes that deliver a constant alternatingcurrent at 50 KHz frequency. Whole body measurements can be done usingstandard right-sided. The ability of any biological tissue to resist aconstant electric current depends on the relative proportions of waterand electrolytes it contains, and is called resistivity (in Ohms/cm³).The measuring of bioimpedance to assess congestive heart failure employsthe different bio-electric properties of blood and lung tissue to permitseparate assessment of: (a) systemic venous congestion via a lowfrequency or direct current resistance measurement of the current paththrough the right ventricle, right atrium, superior vena cava, andsubclavian vein, or by computing the real component of impedance at ahigh frequency, and (b) pulmonary congestion via a high frequencymeasurement of capacitive impedance of the lung. The resistance isimpedance measured using direct current or alternating current (AC)which can flow through capacitors.

In one embodiment, a belt is worn by the patient with a plurality of BIprobes positioned around the belt perimeter. The output of thetetrapolar probes is processed using a second-order Newton-Raphsonmethod to estimate the left and right-lung resistivity values in thethoracic geometry. The locations of the electrodes are marked. Duringthe measurements procedure, the belt is worn around the patient's thoraxwhile sitting, and the reference electrode is attached to his waist. Thedata is collected during tidal respiration to minimize lung resistivitychanges due to breathing, and lasts approximately one minute. Theprocess is repeated periodically and the impedance trend is analyzed todetect CHF. Upon detection, the system provides vital parameters to acall center and the call center can refer to a physician forconsultation or can call 911 for assistance.

In one embodiment, an array of noninvasive thoracic electricalbioimpedance monitoring probes can be used alone or in conjunction withother techniques such as impedance cardiography (ICG) for earlycomprehensive cardiovascular assessment and trending of acute traumavictims. This embodiment provides early, continuous cardiovascularassessment to help identify patients whose injuries were so severe thatthey were not likely to survive. This included severe blood and/or fluidvolume deficits induced by trauma, which did not respond readily toexpeditious volume resuscitation and vasopressor therapy. One exemplarysystem monitors cardiorespiratory variables that served as statisticallysignificant measures of treatment outcomes: Qt, BP, pulse oximetry, andtranscutaneous Po2 (Ptco2). A high Qt may not be sustainable in thepresence of hypovolemia, acute anemia, pre-existing impaired cardiacfunction, acute myocardial injury, or coronary ischemia. Thus a fall inPtco2 could also be interpreted as too high a metabolic demand for apatient's cardiovascular reserve. Too high a metabolic demand maycompromise other critical organs. Acute lung injury from hypotension,blunt trauma, and massive fluid resuscitation can drastically reducerespiratory reserve.

One embodiment that measures thoracic impedance (a resistive or reactiveimpedance associated with at least a portion of a thorax of a livingorganism). The thoracic impedance signal is influenced by the patient'sthoracic intravascular fluid tension, heart beat, and breathing (alsoreferred to as “respiration” or “ventilation”). A “de” or “baseline” or“low frequency” component of the thoracic impedance signal (e.g., lessthan a cutoff value that is approximately between 0.1 Hz and 0.5 Hz,inclusive, such as, for example, a cutoff value of approximately 0.1 Hz)provides information about the subject patient's thoracic fluid tension,and is therefore influenced by intravascular fluid shifts to and awayfrom the thorax. Higher frequency components of the thoracic impedancesignal are influenced by the patient's breathing (e.g., approximatelybetween 0.05 Hz and 2.0 Hz inclusive) and heartbeat (e.g., approximatelybetween 0.5 Hz and 10 Hz inclusive). A low intravascular fluid tensionin the thorax (“thoracic hypotension”) may result from changes inposture. For example, in a person who has been in a recumbent positionfor some time, approximately ⅓ of the blood volume is in the thorax.When that person then sits upright, approximately ⅓ of the blood thatwas in the thorax migrates to the lower body. This increases thoracicimpedance. Approximately 90% of this fluid shift takes place within 2 to3 minutes after the person sits upright.

The accelerometer can be used to provide reproducible measurements. Bodyactivity will increase cardiac output and also change the amount ofblood in the systemic venous system or lungs. Measurements of congestionmay be most reproducible when body activity is at a minimum and thepatient is at rest. The use of an accelerometer allows one to sense bothbody position and body activity. Comparative measurements over time maybest be taken under reproducible conditions of body position andactivity. Ideally, measurements for the upright position should becompared as among themselves. Likewise measurements in the supine,prone, left lateral decubitus and right lateral decubitus should becompared as among themselves. Other variables can be used to permitreproducible measurements, i.e. variations of the cardiac cycle andvariations in the respiratory cycle. The ventricles are at their mostcompliant during diastole. The end of the diastolic period is marked bythe QRS on the electrocardiographic means (EKG) for monitoring thecardiac cycle. The second variable is respiratory variation inimpedance, which is used to monitor respiratory rate and volume. As thelungs fill with air during inspiration, impedance increases, and duringexpiration, impedance decreases. Impedance can be measured duringexpiration to minimize the effect of breathing on central systemicvenous volume. While respiration and CHF both cause variations inimpedance, the rates and magnitudes of the impedance variation aredifferent enough to separate out the respiratory variations which have afrequency of about 8 to 60 cycles per minute and congestion changeswhich take at least several minutes to hours or even days to occur.Also, the magnitude of impedance change is likely to be much greater forcongestive changes than for normal respiratory variation. Thus, thesystem can detect congestive heart failure (CHF) in early stages andalert a patient to prevent disabling and even lethal episodes of CHF.Early treatment can avert progression of the disorder to a dangerousstage.

In an embodiment to monitor wounds such as diabetic related wounds, theconductivity of a region of the patient with a wound or is susceptibleto wound formation is monitored by the system. The system determineshealing wounds if the impedance and reactance of the wound regionincreases as the skin region becomes dry. The system detects infected,open, interrupted healing, or draining wounds through lower regionalelectric impedances. In yet another embodiment, the bioimpedance sensorcan be used to determine body fat. In one embodiment, the BI systemdetermines Total Body Water (TBW) which is an estimate of totalhydration level, including intracellular and extracellular water;Intracellular Water (ICW) which is an estimate of the water in activetissue and as a percent of a normal range (near 60% of TBW);Extracellular Water (ECW) which is water in tissues and plasma and as apercent of a normal range (near 40% of TBW); Body Cell Mass (BCM) whichis an estimate of total pounds/kg of all active cells; ExtracellularTissue (ECT)/Extracellular Mass (ECM) which is an estimate of the massof all other non-muscle inactive tissues including ligaments, bone andECW; Fat Free Mass (FFM)/Lean Body Mass (LBM) which is an estimate ofthe entire mass that is not fat. It should be available in pounds/kg andmay be presented as a percent with a normal range; Fat Mass (FM) whichis an estimate of pounds/kg of body fat and percentage body fat; andPhase Angle (PA) which is associated with both nutrition and physicalfitness.

Additional sensors such as thermocouples or thermisters and/or heat fluxsensors can also be provided to provide measured values useful inanalysis. In general, skin surface temperature will change with changesin blood flow in the vicinity of the skin surface of an organism. Suchchanges in blood flow can occur for a number of reasons, includingthermal regulation, conservation of blood volume, and hormonal changes.In one implementation, skin surface measurements of temperature or heatflux are made in conjunction with hydration monitoring so that suchchanges in blood flow can be detected and appropriately treated.

In one embodiment, the patch includes a sound transducer such as amicrophone or a piezoelectric transducer to pick up sound produced bybones or joints during movement. If bone surfaces are rough and poorlylubricated, as in an arthritic knee, they will move unevenly againsteach other, producing a high-frequency, scratching sound. Thehigh-frequency sound from joints is picked up by wide-band acousticsensor(s) or microphone(s) on a patient's body such as the knee. As thepatient flexes and extends their knee, the sensors measure the soundfrequency emitted by the knee and correlate the sound to monitorosteoarthritis, for example.

In another embodiment, the patch includes a Galvanic Skin Response (GSR)sensor. In this sensor, a small current is passed through one of theelectrodes into the user's body such as the fingers and the CPUcalculates how long it takes for a capacitor to fill up. The length oftime the capacitor takes to fill up allows us to calculate the skinresistance: a short time means low resistance while a long time meanshigh resistance. The GSR reflects sweat gland activity and changes inthe sympathetic nervous system and measurement variables. Measured fromthe palm or fingertips, there are changes in the relative conductance ofa small electrical current between the electrodes. The activity of thesweat glands in response to sympathetic nervous stimulation (Increasedsympathetic activation) results in an increase in the level ofconductance. Fear, anger, startle response, orienting response andsexual feelings are all among the emotions which may produce similar GSRresponses.

In yet another embodiment, measurement of lung function such as peakexpiratory flow readings is done though a sensor such as Wright's peakflow meter. In another embodiment, a respiratory estimator is providedthat avoids the inconvenience of having the patient breathing throughthe flow sensor. In the respiratory estimator embodiment, heart perioddata from EKG/ECG is used to extract respiratory detection features. Theheart period data is transformed into time-frequency distribution byapplying a time-frequency transformation such as short-term Fouriertransformation (STFT). Other possible methods are, for example, complexdemodulation and wavelet transformation. Next, one or more respiratorydetection features may be determined by setting up amplitude modulationof time-frequency plane, among others. The respiratory recognizer firstgenerates a math model that correlates the respiratory detectionfeatures with the actual flow readings. The math model can be adaptivebased on pre-determined data and on the combination of differentfeatures to provide a single estimate of the respiration. The estimatorcan be based on different mathematical functions, such as a curvefitting approach with linear or polynomical equations, and other typesof neural network implementations, non-linear models, fuzzy systems,time series models, and other types of multivariate models capable oftransferring and combining the information from several inputs into oneestimate. Once the math model has been generated, the respiratorestimator provides a real-time flow estimate by receiving EKG/ECGinformation and applying the information to the math model to computethe respiratory rate. Next, the computation of ventilation usesinformation on the tidal volume. An estimate of the tidal volume may bederived by utilizing different forms of information on the basis of theheart period signal. For example, the functional organization of therespiratory system has an impact in both respiratory period and tidalvolume. Therefore, given the known relationships between the respiratoryperiod and tidal volume during and transitions to different states, theinformation inherent in the heart period derived respiratory frequencymay be used in providing values of tidal volume. In specific, the tidalvolume contains inherent dynamics which may be, after modeling, appliedto capture more closely the behavioral dynamics of the tidal volume.Moreover, it appears that the heart period signal, itself, is closelyassociated with tidal volume and may be therefore used to increase thereliability of deriving information on tidal volume. The accuracy of thetidal volume estimation may be further enhanced by using information onthe subjects vital capacity (i.e., the maximal quantity of air that canbe contained in the lungs during one breath). The information on vitalcapacity, as based on physiological measurement or on estimates derivedfrom body measures such as height and weight, may be helpful inestimating tidal volume, since it is likely to reduce the effects ofindividual differences on the estimated tidal volume. Using informationon the vital capacity, the mathematical model may first give values onthe percentage of lung capacity in use, which may be then transformed toliters per breath. The optimizing of tidal volume estimation can basedon, for example, least squares or other type of fit between the featuresand actual tidal volume. The minute ventilation may be derived bymultiplying respiratory rate (breaths/min) with tidal volume(liters/breath).

In another embodiment, inductive plethysmography can be used to measurea cross-sectional area of the body by determining the self-inductance ofa flexible conductor closely encircling the area to be measured. Sincethe inductance of a substantially planar conductive loop is well knownto vary as, inter alia, the cross-sectional area of the loop, ainductance measurement may be converted into a plethysmographic areadetermination. Varying loop inductance may be measured by techniquesknown in the art, such as, e.g., by connecting the loop as theinductance in a variable frequency LC oscillator, the frequency of theoscillator then varying with the cross-sectional area of the loopinductance varies. Oscillator frequency is converted into a digitalvalue, which is then further processed to yield the physiologicalparameters of interest. Specifically, a flexible conductor measuring across-sectional area of the body is closely looped around the area ofthe body so that the inductance, and the changes in inductance, beingmeasured results from magnetic flux through the cross-sectional areabeing measured. The inductance thus depends directly on thecross-sectional area being measured, and not indirectly on an area whichchanges as a result of the factors changing the measured cross-sectionalarea. Various physiological parameters of medical and research interestmay be extracted from repetitive measurements of the areas of variouscross-sections of the body. For example, pulmonary function parameters,such as respiration volumes and rates and apneas and their types, may bedetermined from measurements of, at least, a chest transversecross-sectional area and also an abdominal transverse cross-sectionalarea. Cardiac parameters, such central venous pressure, left and rightventricular volumes waveforms, and aortic and carotid artery pressurewaveforms, may be extracted from repetitive measurements of transversecross-sectional areas of the neck and of the chest passing through theheart. Timing measurements can be obtained from concurrent ECGmeasurements, and less preferably from the carotid pulse signal presentin the neck. From the cardiac-related signals, indications of ischemiamay be obtained independently of any ECG changes. Ventricular wallischemia is known to result in paradoxical wall motion duringventricular contraction (the ischemic segment paradoxically “balloons”outward instead of normally contracting inward). Such paradoxical wallmotion, and thus indications of cardiac ischemia, may be extracted fromchest transverse cross-section area measurements. Left or rightventricular ischemia may be distinguished where paradoxical motion isseen predominantly in left or right ventricular waveforms, respectively.For another example, observations of the onset of contraction in theleft and right ventricles separately may be of use in providing feedbackto bi-ventricular cardiac pacing devices. For a further example, pulseoximetry determines hemoglobin saturation by measuring the changinginfrared optical properties of a finger. This signal may bedisambiguated and combined with pulmonary data to yield improvedinformation concerning lung function.

In one embodiment to monitor and predict stroke attack, a cranialbioimpedance sensor is applied to detect fluids in the brain. The braintissue can be modeled as an electrical circuit where cells with thelipid bilayer act as capacitors and the intra and extra cellular fluidsact as resistors. The opposition to the flow of the electrical currentthrough the cellular fluids is resistance. The system takes 50-kHzsingle-frequency bioimpedance measurements reflecting the electricalconductivity of brain tissue. The opposition to the flow of the currentby the capacitance of lipid bilayer is reactance. In this embodiment,microamps of current at 50 kHz are applied to the electrode system. Inone implementation, the electrode system consists of a pair of coaxialelectrodes each of which has a current electrode and a voltage sensingelectrode. For the measurement of cerebral bioimpedance, one pair of gelcurrent electrodes is placed on closed eyelids and the second pair ofvoltage electrodes is placed in the suboccipital region projectingtowards the foramen magnum. The electrical current passes through theorbital fissures and brain tissue. The drop in voltage is detected bythe suboccipital electrodes and then calculated by the processor tobioimpedance values. The bioimpedance value is used to detect brainedema, which is defined as an increase in the water content of cerebraltissue which then leads to an increase in overall brain mass. Two typesof brain edema are vasogenic or cytotoxic. Vasogenic edema is a resultof increased capillary permeability. Cytotoxic edema reflects theincrease of brain water due to an osmotic imbalance between plasma andthe brain extracellular fluid. Cerebral edema in brain swellingcontributes to the increase in intracranial pressure and an earlydetection leads to timely stroke intervention.

In another example, a cranial bioimpedance tomography system contructsbrain impedance maps from surface measurements using nonlinearoptimization. A nonlinear optimization technique utilizing known andstored constraint values permits reconstruction of a wide range ofconductivity values in the tissue. In the nonlinear system, a JacobianMatrix is renewed for a plurality of iterations. The Jacobian Matrixdescribes changes in surface voltage that result from changes inconductivity. The Jacobian Matrix stores information relating to thepattern and position of measuring electrodes, and the geometry andconductivity distributions of measurements resulting in a normal caseand in an abnormal case. The nonlinear estimation determines the maximumvoltage difference in the normal and abnormal cases.

In one embodiment, an electrode array sensor can include impedance,bio-potential, or electromagnetic field tomography imaging of cranialtissue. The electrode array sensor can be a geometric array of discreteelectrodes having an equally-spaced geometry of multiple nodes that arecapable of functioning as sense and reference electrodes. In a typicaltomography application the electrodes are equally-spaced in a circularconfiguration. Alternatively, the electrodes can have non-equal spacingand/or can be in rectangular or other configurations in one circuit ormultiple circuits. Electrodes can be configured in concentric layerstoo. Points of extension form multiple nodes that are capable offunctioning as an electrical reference. Data from the multiple referencepoints can be collected to generate a spectrographic composite formonitoring over time.

The patient's brain cell generates an electromagnetic field of positiveor negative polarity, typically in the millivolt range. The sensormeasures the electromagnetic field by detecting the difference inpotential between one or more test electrodes and a reference electrode.The bio-potential sensor uses signal conditioners or processors tocondition the potential signal. In one example, the test electrode andreference electrode are coupled to a signal conditioner/processor thatincludes a lowpass filter to remove undesired high frequency signalcomponents. The electromagnetic field signal is typically a slowlyvarying DC voltage signal. The lowpass filter removes undesiredalternating current components arising from static discharge,electromagnetic interference, and other sources.

In one embodiment, the impedance sensor has an electrode structure withannular concentric circles including a central electrode, anintermediate electrode and an outer electrode, all of which areconnected to the skin. One electrode is a common electrode and suppliesa low frequency signal between this common electrode and another of thethree electrodes. An amplifier converts the resulting current into avoltage between the common electrode and another of the threeelectrodes. A switch switches between a first circuit using theintermediate electrode as the common electrode and a second circuit thatuses the outer electrode as a common electrode. The sensor selects depthby controlling the extension of the electric field in the vicinity ofthe measuring electrodes using the control electrode between themeasuring electrodes. The control electrode is actively driven with thesame frequency as the measuring electrodes to a signal level taken fromone of the measuring electrodes but multiplied by a complex number withreal and imaginary parts controlled to attain a desired depthpenetration. The controlling field functions in the manner of a fieldeffect transistor in which ionic and polarization effects act upontissue in the manner of a semiconductor material.

With multiple groups of electrodes and a capability to measure at aplurality of depths, the system can perform tomographic imaging ormeasurement, and/or object recognition. In one embodiment, a fastreconstruction technique is used to reduce computation load by utilizingprior information of normal and abnormal tissue conductivitycharacteristics to estimate tissue condition without requiring fullcomputation of a non-linear inverse solution.

In another embodiment, the bioimpedance system can be used withelectro-encephalograph (EEG) or ERP. Since this embodiment collectssignals related to blood flow in the brain, collection can beconcentrated in those regions of the brain surface corresponding toblood vessels of interest. A headcap with additional electrodes placedin proximity to regions of the brain surface fed by a blood vessel ofinterest, such as the medial cerebral artery enables targetedinformation from the regions of interest to be collected. The headcapcan cover the region of the brain surface that is fed by the medialcerebral artery. Other embodiments of the headcap can concentrateelectrodes on other regions of the brain surface, such as the regionassociated with the somatosensory motor cortex. In alternativeembodiments, the headcap can cover the skull more completely. Further,such a headcap can include electrodes thoughout the cap whileconcentrating electrodes in a region of interest. Depending upon theparticular application, arrays of 1-16 head electrodes may be used, ascompared to the International 10/20 system of 19-21 head electrodesgenerally used in an EEG instrument.

In one implementation, each amplifier for each EEG channel is a highquality analog amplifier device. Full bandwidth and ultra-low noiseamplification are obtained for each electrode. Low pass, high pass, humnotch filters, gain, un-block, calibration and electrode impedance checkfacilities are included in each amplifier. All 8 channels in one EEGamplifier unit have the same filter, gain, etc. settings. Noise figuresof less than 0.1 uV r.m.s. are achieved at the input and opticalcoupling stages. These figures, coupled with good isolation/common moderejection result in signal clarity. Nine high pass filter ranges include0.01 Hz for readiness potential measurement, and 30 Hz for EMGmeasurement.

In one embodiment, stimulations to elicit EEG signals are used in twodifferent modes, i.e., auditory clicks and electric pulses to the skin.The stimuli, although concurrent, are at different prime numberfrequencies to permit separation of different evoked potentials (EPs)and avoid interference. Such concurrent stimulations for EP permit amore rapid, and less costly, examination and provide the patient'sresponses more quickly. Power spectra of spontaneous EEG, waveshapes ofAveraged Evoked Potentials, and extracted measures, such as frequencyspecific power ratios, can be transmitted to a remote receiver. Thelatencies of successive EP peaks of the patient may be compared to thoseof a normal group by use of a normative template.

To test for ischemic stroke or intracerebral or subarachnoid hemorrhage,the system provides a blood oxygen saturation monitor, using aninfra-red or laser source, to alert the user if the patient's blood inthe brain or some brain region is deoxygenated.

A stimulus device may optionally be placed on each subject, such as anaudio generator in the form of an ear plug, which produces a series of“click” sounds. The subject's brain waves are detected and convertedinto audio tones. The device may have an array of LED (Light EmittingDiodes) which blink depending on the power and frequency composition ofthe brain wave signal. Power ratios in the frequencies of audio orsomatosensory stimuli are similarly encoded. The EEG can be transmittedto a remote physician or medical aide who is properly trained todetermine whether the patient's brain function is abnormal and mayevaluate the functional state of various levels of the patient's nervoussystem.

In another embodiment, three pairs of electrodes are attached to thehead of the subject under examination via tape or by wearing a cap withelectrodes embedded. In one embodiment, the electrode pairs are asfollows:

-   -   1) top of head to anterior throat    -   2) inion-nasion    -   3) left to right mastoid (behind ear).

A ground electrode is located at an inactive site of the upper part ofthe vertebral column. The electrodes are connected to differentialamplification devices as disclosed below. Because the electrical chargesof the brain are so small (on the order of microvolts), amplification isneeded. The three amplified analog signals are converted to digitalsignals and averaged over a certain number of successive digital valuesto eliminate erroneous values originated by noise on the analog signal.

All steps defined above are linked to a timing signal which is alsoresponsible for generating stimuli to the subject. The responses areprocessed in a timed relation to the stimuli and averaged as the brainresponds to these stimuli. Of special interest are the responses withincertain time periods and time instances after the occurrence of astimulus of interest. These time periods and instances and theirreferences can be:

-   -   25 to 60 milliseconds: P1-N1    -   180 to 250 milliseconds: N2    -   100 milliseconds: N100    -   200 milliseconds: P2    -   300 milliseconds: P300.

In an examination two stimuli sets may be used in a manner that thebrain has to respond to the two stimuli differently, one stimulus has ahigh probability of occurrence, and the other stimulus is a rareoccurring phenomena. The rare response is the response of importance.Three response signals are sensed and joined into a three dimensionalcartesian system by a mapping program. The assignments can be

-   -   nasion-inion=X,    -   left-right mastoid=Y, and    -   top of head to anterior throat=Z.

The assignment of the probes to the axes and the simultaneous samplingof the three response signals at the same rate and time relative to thestimuli allows to real-time map the electrical signal in a threedimensional space. The signal can be displayed in a perspectiverepresentation of the three dimensional space, or the three componentsof the vector are displayed by projecting the vector onto the threeplanes X-Y, Y-Z, and X-Z, and the three planes are inspected together orseparately. Spatial information is preserved for reconstruction as amap. The Vector Amplitude (VA) measure provides information about howfar from the center of the head the observed event is occurring; thecenter of the head being the center (0,0,0) of the coordinate system.

The cranial bioimpedance sensor can be applied singly or in combinationwith a cranial blood flow sensor, which can be optical, ultrasound,electromagnetic sensor(s) as described in more details below. In anultrasound imaging implementation, the carotid artery is checked forplaque build-up. Atherosclerosis is systemic—meaning that if the carotidartery has plaque buildup, other important arteries, such as coronaryand leg arteries, might also be atherosclerotic.

In another embodiment, an epicardial array monopolar ECG system convertssignals into the multichannel spectrum domain and identifies decisionvariables from the autospectra. The system detects and localizes theepicardial projections of ischemic myocardial ECGs during the cardiacactivation phase. This is done by transforming ECG signals from anepicardial or torso sensor array into the multichannel spectral domainand identifying any one or more of a plurality of decision variables.The ECG array data can be used to detect, localize and quantifyreversible myocardial ischemia.

In yet another embodiment, a trans-cranial Doppler velocimetry sensorprovides a non-invasive technique for measuring blood flow in the brain.An ultrasound beam from a transducer is directed through one of threenatural acoustical windows in the skull to produce a waveform of bloodflow in the arteries using Doppler sonography. The data collected todetermine the blood flow may include values such as the pulse cycle,blood flow velocity, end diastolic velocity, peak systolic velocity,mean flow velocity, total volume of cerebral blood flow, flowacceleration, the mean blood pressure in an artery, and the pulsatilityindex, or impedance to flow through a vessel. From this data, thecondition of an artery may be derived, those conditions includingstenosis, vasoconstriction, irreversible stenosis, vasodilation,compensatory vasodilation, hyperemic vasodilation, vascular failure,compliance, breakthrough, and pseudo-normalization.

In addition to the above techniques to detect stroke attack, the systemcan detect numbness or weakness of the face, arm or leg, especially onone side of the body. The system detects sudden confusion, troublespeaking or understanding, sudden trouble seeing in one or both eyes,sudden trouble walking, dizziness, loss of balance or coordination, orsudden, severe headache with no known cause.

In one embodiment to detect heart attack, the system detects discomfortin the center of the chest that lasts more than a few minutes, or thatgoes away and comes back. Symptoms can include pain or discomfort in oneor both arms, the back, neck, jaw or stomach. The system can alsomonitor for shortness of breath which may occur with or without chestdiscomfort. Other signs may include breaking out in a cold sweat, nauseaor lightheadedness.

In order to best analyze a patient's risk of stroke, additional patientdata is utilized by a stroke risk analyzer. This data may includepersonal data, such as date of birth, ethnic group, sex, physicalactivity level, and address. The data may further include clinical datasuch as a visit identification, height, weight, date of visit, age,blood pressure, pulse rate, respiration rate, and so forth. The data mayfurther include data collected from blood work, such as the antinuclearantibody panel, B-vitamin deficiency, C-reactive protein value, calciumlevel, cholesterol levels, entidal CO.sub.2, fibromogin, amount of folicacid, glucose level, hematocrit percentage, H-pylori antibodies,hemocysteine level, hypercapnia, magnesium level, methyl maloric acidlevel, platelets count, potassium level, sedrate (ESR), serumosmolality, sodium level, zinc level, and so forth. The data may furtherinclude the health history data of the patient, including alcoholintake, autoimmune diseases, caffeine intake, carbohydrate intake,carotid artery disease, coronary disease, diabetes, drug abuse,fainting, glaucoma, head injury, hypertension, lupus, medications,smoking, stroke, family history of stroke, surgery history, for example.

In one embodiment, data driven analyzers may be used to track thepatient's risk of stroke or heart attack. These data driven analyzersmay incorporate a number of models such as parametric statisticalmodels, non-parametric statistical models, clustering models, nearestneighbor models, regression methods, and engineered (artificial) neuralnetworks. Prior to operation, data driven analyzers or models of thepatient stoke patterns are built using one or more training sessions.The data used to build the analyzer or model in these sessions aretypically referred to as training data. As data driven analyzers aredeveloped by examining only training examples, the selection of thetraining data can significantly affect the accuracy and the learningspeed of the data driven analyzer. One approach used heretoforegenerates a separate data set referred to as a test set for trainingpurposes. The test set is used to avoid overfitting the model oranalyzer to the training data. Overfitting refers to the situation wherethe analyzer has memorized the training data so well that it fails tofit or categorize unseen data. Typically, during the construction of theanalyzer or model, the analyzer's performance is tested against the testset. The selection of the analyzer or model parameters is performediteratively until the performance of the analyzer in classifying thetest set reaches an optimal point. At this point, the training processis completed. An alternative to using an independent training and testset is to use a methodology called cross-validation. Cross-validationcan be used to determine parameter values for a parametric analyzer ormodel for a non-parametric analyzer. In cross-validation, a singletraining data set is selected. Next, a number of different analyzers ormodels are built by presenting different parts of the training data astest sets to the analyzers in an iterative process. The parameter ormodel structure is then determined on the basis of the combinedperformance of all models or analyzers. Under the cross-validationapproach, the analyzer or model is typically retrained with data usingthe determined optimal model structure.

In general, multiple dimensions of a user's EEG, EKG, BI, ultrasound,optical, acoustic, electromagnetic, or electrical parameters are encodedas distinct dimensions in a database. A predictive model, including timeseries models such as those employing autoregression analysis and otherstandard time series methods, dynamic Bayesian networks and ContinuousTime Bayesian Networks, or temporal Bayesian-network representation andreasoning methodology, is built, and then the model, in conjunction witha specific query makes target inferences. Bayesian networks provide notonly a graphical, easily interpretable alternative language forexpressing background knowledge, but they also provide an inferencemechanism; that is, the probability of arbitrary events can becalculated from the model. Intuitively, given a Bayesian network, thetask of mining interesting unexpected patterns can be rephrased asdiscovering item sets in the data which are much more—or muchless—frequent than the background knowledge suggests. These cases areprovided to a learning and inference subsystem, which constructs aBayesian network that is tailored for a target prediction. The Bayesiannetwork is used to build a cumulative distribution over events ofinterest.

In another embodiment, a genetic algorithm (GA) search technique can beused to find approximate solutions to identifying the user's strokerisks or heart attack risks. Genetic algorithms are a particular classof evolutionary algorithms that use techniques inspired by evolutionarybiology such as inheritance, mutation, natural selection, andrecombination (or crossover). Genetic algorithms are typicallyimplemented as a computer simulation in which a population of abstractrepresentations (called chromosomes) of candidate solutions (calledindividuals) to an optimization problem evolves toward better solutions.Traditionally, solutions are represented in binary as strings of 0s and1s, but different encodings are also possible. The evolution starts froma population of completely random individuals and happens ingenerations. In each generation, the fitness of the whole population isevaluated, multiple individuals are stochastically selected from thecurrent population (based on their fitness), modified (mutated orrecombined) to form a new population, which becomes current in the nextiteration of the algorithm.

Substantially any type of learning system or process may be employed todetermine the stroke or heart attack patterns so that unusual events canbe flagged.

In one embodiment, clustering operations are performed to detectpatterns in the data. In another embodiment, a neural network is used torecognize each pattern as the neural network is quite robust atrecognizing user habits or patterns. Once the treatment features havebeen characterized, the neural network then compares the input userinformation with stored templates of treatment vocabulary known by theneural network recognizer, among others. The recognition models caninclude a Hidden Markov Model (HMM), a dynamic programming model, aneural network, a fuzzy logic, or a template matcher, among others.These models may be used singly or in combination.

Dynamic programming considers all possible points within the permitteddomain for each value of i. Because the best path from the current pointto the next point is independent of what happens beyond that point.Thus, the total cost of [i(k), j(k)] is the cost of the point itselfplus the cost of the minimum path to it. Preferably, the values of thepredecessors can be kept in an M×N array, and the accumulated cost keptin a 2×N array to contain the accumulated costs of the immediatelypreceding column and the current column. However, this method requiressignificant computing resources. For the recognizer to find the optimaltime alignment between a sequence of frames and a sequence of nodemodels, it must compare most frames against a plurality of node models.One method of reducing the amount of computation required for dynamicprogramming is to use pruning Pruning terminates the dynamic programmingof a given portion of user habit information against a given treatmentmodel if the partial probability score for that comparison drops below agiven threshold. This greatly reduces computation.

Considered to be a generalization of dynamic programming, a hiddenMarkov model is used in the preferred embodiment to evaluate theprobability of occurrence of a sequence of observations O(1), O(2), . .. O(t), . . . , O(T), where each observation O(t) may be either adiscrete symbol under the VQ approach or a continuous vector. Thesequence of observations may be modeled as a probabilistic function ofan underlying Markov chain having state transitions that are notdirectly observable. In one embodiment, the Markov network is used tomodel a number of user habits and activities. The transitions betweenstates are represented by a transition matrix A=[a(i,j)]. Each a(i,j)term of the transition matrix is the probability of making a transitionto state j given that the model is in state i. The output symbolprobability of the model is represented by a set of functions B=[b(j)(O(t)], where the b(j) (O(t) term of the output symbol matrix is theprobability of outputting observation O(t), given that the model is instate j. The first state is always constrained to be the initial statefor the first time frame of the utterance, as only a prescribed set ofleft to right state transitions are possible. A predetermined finalstate is defined from which transitions to other states cannot occur.Transitions are restricted to reentry of a state or entry to one of thenext two states. Such transitions are defined in the model as transitionprobabilities. Although the preferred embodiment restricts the flowgraphs to the present state or to the next two states, one skilled inthe art can build an HMM model without any transition restrictions,although the sum of all the probabilities of transitioning from anystate must still add up to one. In each state of the model, the currentfeature frame may be identified with one of a set of predefined outputsymbols or may be labeled probabilistically. In this case, the outputsymbol probability b(j) O(t) corresponds to the probability assigned bythe model that the feature frame symbol is O(t). The model arrangementis a matrix A=[a(i,j)] of transition probabilities and a technique ofcomputing B=b(j) O(t), the feature frame symbol probability in state j.The Markov model is formed for a reference pattern from a plurality ofsequences of training patterns and the output symbol probabilities aremultivariate Gaussian function probability densities. The patient habitinformation is processed by a feature extractor. During learning, theresulting feature vector series is processed by a parameter estimator,whose output is provided to the hidden Markov model. The hidden Markovmodel is used to derive a set of reference pattern templates, eachtemplate representative of an identified pattern in a vocabulary set ofreference treatment patterns. The Markov model reference templates arenext utilized to classify a sequence of observations into one of thereference patterns based on the probability of generating theobservations from each Markov model reference pattern template. Duringrecognition, the unknown pattern can then be identified as the referencepattern with the highest probability in the likelihood calculator. TheHMM template has a number of states, each having a discrete value.However, because treatment pattern features may have a dynamic patternin contrast to a single value. The addition of a neural network at thefront end of the HMM in an embodiment provides the capability ofrepresenting states with dynamic values. The input layer of the neuralnetwork comprises input neurons. The outputs of the input layer aredistributed to all neurons in the middle layer. Similarly, the outputsof the middle layer are distributed to all output states, which normallywould be the output layer of the neuron. However, each output hastransition probabilities to itself or to the next outputs, thus forminga modified HMM. Each state of the thus formed HMM is capable ofresponding to a particular dynamic signal, resulting in a more robustHMM. Alternatively, the neural network can be used alone withoutresorting to the transition probabilities of the HMM architecture.

The automated analyzer can also consider related pathologies inanalyzing a patient's risk of stroke, including but not limited togastritis, increased intracranial pressure, sleep disorders, smallvessel disease, and vasculitis.

In one embodiment, the processor and transceiver on the watch, thepatch(es) and the base station conform to the Zigbee protocol. ZigBee isa cost-effective, standards-based wireless networking solution thatsupports low data-rates, low-power consumption, security, andreliability. Single chip Zigbee controllers with wireless transceiversbuilt-in include the Chipcon/Ember CC2420: Single-chip 802.15.4 radiotransceiver and the FreeScale single chip Zigbee and microcontroller. Invarious embodiments, the processor communicates with a Z axisaccelerometer measures the patient's up and down motion and/or an X andY axis accelerometer measures the patient's forward and side movements.In one embodiment, EKG and/or blood pressure parameters can be capturedby the processor. The controllers upload the captured data when thememory is full or while in wireless contact with other Zigbee nodes.

The wristwatch device can also be used to control home automation. Theuser can have flexible management of lighting, heating and coolingsystems from anywhere in the home. The watch automates control ofmultiple home systems to improve conservation, convenience and safety.The watch can capture highly detailed electric, water and gas utilityusage data and embed intelligence to optimize consumption of naturalresources. The system is convenient in that it can be installed,upgraded and networked without wires. The patient can receive automaticnotification upon detection of unusual events in his or her home. Forexample, if smoke or carbon monoxide detectors detect a problem, thewrist-watch can buzz or vibrate to alert the user and the central hubtriggers selected lights to illuminate the safest exit route.

In another embodiment, the watch serves a key fob allowing the user towirelessly unlock doors controlled by Zigbee wireless receiver. In thisembodiment, when the user is within range, the door Zigbee transceiverreceives a request to unlock the door, and the Zigbee transceiver on thedoor transmits an authentication request using suitable securitymechanism. Upon entry, the Zigbee doorlock device sends access signalsto the lighting, air-conditioning and entertainment systems, amongothers. The lights and temperature are automatically set topre-programmed preferences when the user's presence is detected.

Although Zigbee is mentioned as an exemplary protocol, other protocolssuch as UWB, Bluetooth, WiFi and WiMAX can be used as well.

While the foregoing addresses the needs of the elderly, the system canassist infants as well. Much attention has been given to ways to reducea risk of dying from Sudden Infant Death Syndrome (SIDS), an afflictionwhich threatens infants who have died in their sleep for heretoforeunknown reasons. Many different explanations for this syndrome and waysto prevent the syndrome are found in the literature. It is thought thatinfants which sleep on their backs may be at risk of death because ofthe danger of formula regurgitation and liquid aspiration into thelungs. It has been thought that infants of six (6) months or less do nothave the motor skills or body muscular development to regulate movementsresponsive to correcting breathing problems that may occur during sleep.

In an exemplary system to detect and minimize SIDS problem in an infantpatient, a diaper pad is used to hold an array of integrated sensors andthe pad can be placed over a diaper, clothing, or blanket. Theintegrated sensors can provide data for measuring position, temperature,sound, vibration, movement, and optionally other physical propertiesthrough additional sensors. Each pad can have sensors that provide oneor more of the above data. The sensors can be added or removed asnecessary depending on the type of data being collected.

The sensor can be water proof and disposable. The sensor can be switchon/off locally or remotely. The sensor can be removable or clip oneasily. The sensor can store or beam out information for analysispurpose, e.g. store body temperature every 5 seconds. The sensor can beturn-on for other purposed, e.g. diaper wet, it will beep and allow ababy care provider to take care of the business in time. The array ofsensors can be self selective, e.g., when one sensor can detect strongheart beat, it will turn off others to do so.

The sensor can be used for drug delivery system, e.g. when patient hasabdomen pain, soothing drug can be applied, based on the level of painthe sensor detects, different dose of drugs will be applied.

The array of sensors may allow the selection and analysis of zones ofsensors in the areas of interest such as the abdomen area. Each sensorarray has a low spatial resolution: approximately 10 cm between eachsensor. In addition to lower cost due to the low number of sensors, itis also possible to modify the data collection rate from certain sensorsthat are providing high-quality data. Other sensors may include thoseworn on the body, such as in watch bands, finger rings, or adhesivesensors, but telemetry, not wires, would be used to communicate with thecontroller.

The sensor can be passive device such as a reader, which mounted nearthe crib can active it from time to time. In any emergency situation,the sensor automatically signals a different state which the reader candetect.

The sensor can be active and powered by body motion or body heat. Thesensor can detect low battery situation and warn the user to provide areplacement battery. In one embodiment, a plurality of sensors attachedto the infant collects the vital parameters. For example, the sensorscan be attached to the infant's clothing (shirt or pant), diaper,undergarment or bed sheet, bed linen, or bed spread.

The patient may wear one or more sensors, for example devices forsensing EMG, EKG, blood pressure, sugar level, weight, temperature andpressure, among others. In one embodiment, an optical temperature sensorcan be used. In another embodiment, a temperature thermistor can be usedto sense patient temperature. In another embodiment, a fat scale sensorcan be used to detect the patient's fat content. In yet anotherembodiment, a pressure sensor such as a MEMS sensor can be used to sensepressure on the patient.

In one embodiment, the sensors are mounted on the patient's wrist (suchas a wristwatch sensor) and other convenient anatomical locations.Exemplary sensors include standard medical diagnostics for detecting thebody's electrical signals emanating from muscles (EMG and EOG) and brain(EEG) and cardiovascular system (ECG). Leg sensors can includepiezoelectric accelerometers designed to give qualitative assessment oflimb movement. Additionally, thoracic and abdominal bands used tomeasure expansion and contraction of the thorax and abdomenrespectively. A small sensor can be mounted on the subject's finger inorder to detect blood-oxygen levels and pulse rate. Additionally, amicrophone can be attached to throat and used in sleep diagnosticrecordings for detecting breathing and other noise. One or more positionsensors can be used for detecting orientation of body (lying on leftside, right side or back) during sleep diagnostic recordings. Each ofsensors can individually transmit data to the server 20 using wired orwireless transmission. Alternatively, all sensors can be fed through acommon bus into a single transceiver for wired or wireless transmission.The transmission can be done using a magnetic medium such as a floppydisk or a flash memory card, or can be done using infrared or radionetwork link, among others.

In one embodiment, the sensors for monitoring vital signs are enclosedin a wrist-watch sized case supported on a wrist band. The sensors canbe attached to the back of the case. For example, in one embodiment,Cygnus' AutoSensor (Redwood City, Calif.) is used as a glucose sensor. Alow electric current pulls glucose through the skin. Glucose isaccumulated in two gel collection discs in the AutoSensor. TheAutoSensor measures the glucose and a reading is displayed by the watch.

In another embodiment, EKG/ECG contact points are positioned on the backof the wrist-watch case. In yet another embodiment that providescontinuous, beat-to-beat wrist arterial pulse rate measurements, apressure sensor is housed in a casing with a ‘free-floating’ plunger asthe sensor applanates the radial artery. A strap provides a constantforce for effective applanation and ensuring the position of the sensorhousing to remain constant after any wrist movements. The change in theelectrical signals due to change in pressure is detected as a result ofthe piezoresistive nature of the sensor are then analyzed to arrive atvarious arterial pressure, systolic pressure, diastolic pressure, timeindices, and other blood pressure parameters.

The heartbeat detector can be one of: EKG detector, ECG detector,optical detector, ultrasonic detector, or microphone/digital stethoscopefor picking up heart sound. In one embodiment, one EKG/ECG contact pointis provided on the back of the wrist watch case and one or more EKG/ECGcontact points are provided on the surface of the watch so that when auser's finger or skin touches the contact points, an electrical signalindicative of heartbeat activity is generated. An electrocardiogram(ECG) or EKG is a graphic tracing of the voltage generated by thecardiac or heart muscle during a heartbeat. It provides very accurateevaluation of the performance of the heart. The heart generates anelectrochemical impulse that spreads out in the heart in such a fashionas to cause the cells to contract and relax in a timely order and thusgive the heart a pumping characteristic. This sequence is initiated by agroup of nerve cells called the sinoatrial (SA) node resulting in apolarization and depolarization of the cells of the heart. Because thisaction is electrical in nature and because the body is conductive withits fluid content, this electrochemical action can be measured at thesurface of the body. An actual voltage potential of approximately lmVdevelops between various body points. This can be measured by placingelectrode contacts on the body. The four extremities and the chest wallhave become standard sites for applying the electrodes. Standardizingelectrocardiograms makes it possible to compare them as taken fromperson to person and from time to time from the same person. The normalelectrocardiogram shows typical upward and downward deflections thatreflect the alternate contraction of the atria (the two upper chambers)and of the ventricles (the two lower chambers) of the heart. Thevoltages produced represent pressures exerted by the heart muscles inone pumping cycle. The first upward deflection, P, is due to atriacontraction and is known as the atrial complex. The other deflections,Q, R, S, and T, are all due to the action of the ventricles and areknown as the ventricular complexes. Any deviation from the norm in aparticular electrocardiogram is indicative of a possible heart disorder.

The CPU measures the time duration between the sequential pulses andconverts each such measurement into a corresponding timing measurementindicative of heart rate. The CPU also processes a predetermined numberof most recently occurring timing measurements in a prescribed fashion,to produce an estimate of heartbeat rate for display on a display deviceon the watch and/or for transmission over the wireless network. Thisestimate is updated with the occurrence of each successive pulse.

In one embodiment, the CPU produces the estimate of heartbeat rate byfirst averaging a plurality of measurements, then adjusting theparticular one of the measurements that differs most from the average tobe equal to that average, and finally computing an adjusted averagebased on the adjusted set of measurements. The process may repeat theforegoing operations a number of times so that the estimate of heartbeatrate is substantially unaffected by the occurrence of heartbeatartifacts.

In one EKG or ECG detector, the heartbeat detection circuitry includes adifferential amplifier for amplifying the signal transmitted from theEKG/ECG electrodes and for converting it into single-ended form, and abandpass filter and a 60 Hz notch filter for removing background noise.The CPU measures the time durations between the successive pulses andestimates the heartbeat rate. The time durations between the successivepulses of the pulse sequence signal provides an estimate of heartbeatrate. Each time duration measurement is first converted to acorresponding rate, preferably expressed in beats per minute (bpm), andthen stored in a file, taking the place of the earliest measurementpreviously stored. After a new measurement is entered into the file, thestored measurements are averaged, to produce an average ratemeasurement. The CPU optionally determines which of the storedmeasurements differs most from the average, and replaces thatmeasurement with the average.

Upon initiation, the CPU increments a period timer used in measuring thetime duration between successive pulses. This timer is incremented insteps of about two milliseconds in one embodiment. It is then determinedwhether or not a pulse has occurred during the previous twomilliseconds. If it has not, the CPU returns to the initial step ofincrementing the period timer. If a heartbeat has occurred, on the otherhand, the CPU converts the time duration measurement currently stored inthe period timer to a corresponding heartbeat rate, preferably expressedin bpm. After the heartbeat rate measurement is computed, the CPUdetermines whether or not the computed rate is intermediate prescribedthresholds of 20 bpm and 240 bpm. If it is not, it is assumed that thedetected pulse was not in fact a heartbeat and the period timer iscleared.

In an optical heartbeat detector embodiment, an optical transducer ispositioned on a finger, wrist, or ear lobe. The ear, wrist or fingerpulse oximeter waveform is then analyzed to extract the beat-to-beatamplitude, area, and width (half height) measurements. The oximeterwaveform is used to generate heartbeat rate in this embodiment. In oneimplementation, a reflective sensor such as the Honeywell HLC1395 can beused. The device emits lights from a window in the infrared spectrum andreceives reflected light in a second window. When the heart beats, bloodflow increases temporarily and more red blood cells flow through thewindows, which increases the light reflected back to the detector. Thelight can be reflected, refracted, scattered, and absorbed by one ormore detectors. Suitable noise reduction is done, and the resultingoptical waveform is captured by the CPU.

In another optical embodiment, blood pressure is estimated from theoptical reading using a mathematical model such as a linear correlationwith a known blood pressure reading. In this embodiment, the pulseoximeter readings are compared to the blood-pressure readings from aknown working blood pressure measurement device during calibration.Using these measurements the linear equation is developed relatingoximeter output waveform such as width to blood-pressure (systolic, meanand pulse pressure). In one embodiment, a transform (such as a Fourieranalysis or a Wavelet transform) of the oximeter output can be used togenerate a model to relate the oximeter output waveform to the bloodpressure. Other non-linear math model or relationship can be determinedto relate the oximeter waveform to the blood pressure.

In one implementation, the pulse oximeter probe and a blood pressurecuff are placed on the corresponding contralateral limb to theoscillometric (Dinamap 8100; Critikon, Inc, Tampa, Fla., USA) cuff site.The pulse oximeter captures data on plethysmographic waveform, heartrate, and oxygen saturation. Simultaneous blood pressure measurementswere obtained from the oscillometric device, and the pulse oximeter.Systolic, diastolic, and mean blood pressures are recorded from theoscillometric device. This information is used derive calibrationparameters relating the pulse oximeter output to the expected bloodpressure. During real time operation, the calibration parameters areapplied to the oximeter output to predict blood pressure in a continuousor in a periodic fashion. In yet another embodiment, the device includesan accelerometer or alternative motion-detecting device to determinewhen the patient' hand is at rest, thereby reducing motion-relatedartifacts introduced to the measurement during calibration and/oroperation. The accelerometer can also function as a falls detectiondevice.

In an ultrasonic embodiment, a piezo film sensor element is placed onthe wristwatch band. The sensor can be the SDT1-028K made by MeasurementSpecialties, Inc. The sensor should have features such as: (a) it issensitive to low level mechanical movements, (b) it has an electrostaticshield located on both sides of the element (to minimize 50/60 Hz ACline interference), (c) it is responsive to low frequency movements inthe 0.7-12 Hz range of interest. A filter/amplifier circuit has athree-pole low pass filter with a lower (−3 dB) cutoff frequency atabout 12-13 Hz. The low-pass filter prevents unwanted 50/60 Hz AC lineinterference from entering the sensor. However, the piezo film elementhas a wide band frequency response so the filter also attenuates anyextraneous sound waves or vibrations that get into the piezo element.The DC gain is about +30 dB.

Waveform averaging can be used to reduce noise. It reinforces thewaveform of interest by minimizing the effect of any random noise. Thesepulses were obtained when the arm was motionless. If the arm was movedwhile capturing the data the waveform did not look nearly as clean.That's because motion of the arm causes the sonic vibrations to enterthe piezo film through the arm or by way of the cable. An accelerometeris used to detect arm movement and used to remove inappropriate datacapture.

In one embodiment that can determine blood pressure, two piezo filmsensors and filter/amplifier circuits can be configured as anon-invasive velocity type blood pressure monitor. One sensor can be onthe wrist and the other can be located on the inner left elbow at thesame location where Korotkoff sounds are monitored during traditionalblood pressure measurements with a spygmometer. The correlation betweenpulse delay and blood pressure is well known in the art of non-invasiveblood pressure monitors.

In yet another embodiment, an ultrasonic transducer generates andtransmits an acoustic wave into the user's body such as the wrist orfinger. The transducer subsequently receives pressure waves in the formof echoes resulting from the transmitted acoustic waves. In oneembodiment, an ultrasonic driving and receiving circuit generateselectrical pulses which, when applied to the transducer produce acousticenergy having a frequency on the order of 8 MHz, a pulse width orduration of approximately 8 microseconds, and a pulse repetitioninterval (PRI) of approximately 16 microseconds, although other valuesof frequency, pulse width, and PRI may be used. Hence, the transduceremits an 8 microsecond ultrasonic pulse, which is followed by an 8microsecond “listen” period, every 16 microseconds. The echoes fromthese pulses are received by the ultrasonic transducer during the listenperiod. The ultrasonic transducer can be a ceramic piezoelectric deviceof the type well known in the art, although other types may besubstituted. The transducer converts the received acoustic signal to anelectrical signal, which is then supplied to the receiving section ofthe ultrasonic driver and receiver circuit 616, which contains tworeceiver circuits. The output of the first receiver circuit is an analogsignal representative of the Doppler frequency of the echo received bythe transducer which is digitized and supplied to the CPU. Within theCPU, the digitized Doppler frequency is scaled to compute the bloodvelocity within the artery based on the Doppler frequency. Thetime-frequency distribution of the blood velocity is then computed.Finally, the CPU maps in time the peak of the time-frequencydistribution to the corresponding pressure waveform to produce theestimated mean arterial pressure (MAP). The output of the ultrasonicreceiver circuit is an analog echo signal proportional to absorption ofthe transmitted frequencies by blood or tissue. This analog signal isdigitized and process so that each group of echoes, generated for adifferent transversal position, is integrated to determine a mean value.The mean echo values are compared to determine the minimum value, whichis caused by direct positioning over the artery. In one embodiment, thedevice includes an accelerometer or alternative motion-detecting deviceto determine when the patient' hand is at rest, thereby reducingmotion-related artifacts introduced to the measurement.

In yet another ultrasonic embodiment, a transducer includes a first anda second piezoelectric crystal, wherein the crystals are positioned atan angle to each other, and wherein the angle is determined based on thedistance of the transducer to the living subject. The firstpiezoelectric crystal is energized by an original ultrasonic frequencysignal, wherein the original ultrasonic frequency signal is reflectedoff the living subject and received by the second piezoelectric crystal.More specifically, the system includes a pair of piezoelectric crystalsat an angle to each other, wherein the angle is determined by the depthof the object being monitored. If the object is the radial artery of ahuman subject (e.g., adult, infant), the angle of the two crystals withrespect to the direction of the blood flow would be about 5 to about 20degrees. One of the crystals is energized at an ultrasonic frequency.The signal is then reflected back by the user's wrist and picked up bythe second crystal. The frequency received is either higher or lowerthan the original frequency depending upon the direction and the speedof the fluidic mass flow. For example, when blood flow is monitored, thedirection of flow is fixed. Thus, the Doppler frequency which is thedifference between the original and the reflected frequency depends onlyupon the speed of the blood flow. Ultrasonic energy is delivered to oneof the two piezoelectric elements in the module by the power amplifier.The other element picks up the reflected ultrasonic signal as Dopplerfrequencies.

In a digital stethoscope embodiment, a microphone or a piezoelectrictransducer is placed near the wrist artery to pick up heart rateinformation. In one embodiment, the microphone sensor and optionally theEKG sensor are place on the wrist band 1374 of the watch to analyze theacoustic signal or signals emanating from the cardiovascular system and,optionally can combine the sound with an electric signal (EKG) emanatingfrom the cardiovascular system and/or an acoustic signal emanating fromthe respiratory system. The system can perform automated auscultation ofthe cardiovascular system, the respiratory system, or both. For example,the system can differentiate pathological from benign heart murmurs,detect cardiovascular diseases or conditions that might otherwise escapeattention, recommend that the patient go through for a diagnostic studysuch as an echocardiography or to a specialist, monitor the course of adisease and the effects of therapy, decide when additional therapy orintervention is necessary, and providing a more objective basis for thedecision(s) made. In one embodiment, the analysis includes selecting oneor more beats for analysis, wherein each beat comprises an acousticsignal emanating from the cardiovascular system; performing atime-frequency analysis of beats selected for analysis so as to provideinformation regarding the distribution of energy, the relativedistribution of energy, or both, over different frequency ranges at oneor more points in the cardiac cycle; and processing the information toreach a clinically relevant conclusion or recommendation. In anotherimplementation, the system selects one or more beats for analysis,wherein each beat comprises an acoustic signal emanating from thecardiovascular system; performs a time-frequency analysis of beatsselected for analysis so as to provide information regarding thedistribution of energy, the relative distribution of energy, or both,over different frequency ranges at one or more points in the cardiaccycle; and present information derived at least in part from theacoustic signal, wherein the information comprises one or more itemsselected from the group consisting of: a visual or audio presentation ofa prototypical beat, a display of the time-frequency decomposition ofone or more beats or prototypical beats, and a playback of the acousticsignal at a reduced rate with preservation of frequency content.

In an electromagnetic embodiment where the wrist band incorporates aflexible magnet to provide a magnetic field and one or more electrodespositioned on the wrist band to measure voltage drops which areproportional to the blood velocity, instantaneously variation of theflow can be detected but not artery flow by itself. To estimate the flowof blood in the artery, the user or an actuator such as motorized cufftemporarily stops the blood flow in the vein by applying externalpressure or by any other method. During the period of time in which thevein flow is occluded, the decay of the artery flow is measured. Thismeasurement may be used for zeroing the sensor and may be used in amodel for estimating the steady artery flow. The decay in artery flowdue to occlusion of veins is measured to arrive at a model the rate ofartery decay. The system then estimates an average artery flow beforeocclusion. The blood flow can then be related to the blood pressure.

In another embodiment, an ionic flow sensor is used with a drivingelectrode that produces a pulsatile current. The pulsatile currentcauses a separation of positive and negative charges that flows in theblood of the arteries and veins passing in the wrist area. Usingelectrophoresis principle, the resistance of the volume surrounded bythe source first decreases and then increases. The difference inresistance in the blood acts as a mark that moves according to the flowof blood so that marks are flowing in opposite directions by arteriesand veins.

In the above embodiments, accelerometer information is used to detectthat the patient is at rest prior to making a blood pressure measurementand estimation. Further, a temperature sensor may be incorporated sothat the temperature is known at any minute. The processor correlatesthe temperature measurement to the blood flow measurement forcalibration purposes.

In another embodiment, the automatic identification of the first,second, third and fourth heart sounds (S1, S2, S3, S4) is done. In yetanother embodiment, based on the heart sound, the system analyzes thepatient for mitral valve prolapse. The system performs a time-frequencyanalysis of an acoustic signal emanating from the subject'scardiovascular system and examines the energy content of the signal inone or more frequency bands, particularly higher frequency bands, inorder to determine whether a subject suffers from mitral valve prolapse.

FIG. 7 shows an exemplary mesh network that includes the wrist-watch ofFIG. 6 in communication with a mesh network including a telephone suchas a wired telephone as well as a cordless telephone. In one embodiment,the mesh network is an IEEE 802.15.4 (ZigBee) network. IEEE 802.15.4defines two device types; the reduced function device (RFD) and the fullfunction device (FFD). In ZigBee these are referred to as the ZigBeePhysical Device types. In a ZigBee network a node can have three roles:ZigBee Coordinator, ZigBee Router, and ZigBee End Device. These are theZigBee Logical Device types. The main responsibility of a ZigBeeCoordinator is to establish a network and to define its main parameters(e.g. choosing a radio-frequency channel and defining a unique networkidentifier). One can extend the communication range of a network byusing ZigBee Routers. These can act as relays between devices that aretoo far apart to communicate directly. ZigBee End Devices do notparticipate in routing. An FFD can talk to RFDs or other FFDs, while anRFD can talk only to an FFD. An RFD is intended for applications thatare extremely simple, such as a light switch or a passive infraredsensor; they do not have the need to send large amounts of data and mayonly associate with a single FFD at a time. Consequently, the RFD can beimplemented using minimal resources and memory capacity and have lowercost than an FFD. An FFD can be used to implement all three ZigBeeLogical Device types, while an RFD can take the role as an End Device.

One embodiment supports a multicluster-multihop network assembly toenable communication among every node in a distribution of nodes. Thealgorithm should ensure total connectivity, given a network distributionthat will allow total connectivity. One such algorithm of an embodimentis described in U.S. Pat. No. 6,832,251, the content of which isincorporated by referenced. The '251 algorithm runs on each nodeindependently. Consequently, the algorithm does not have globalknowledge of network topology, only local knowledge of its immediateneighborhood. This makes it well suited to a wide variety ofapplications in which the topology may be time-varying, and the numberof nodes may be unknown. Initially, all nodes consider themselvesremotes on cluster zero. The assembly algorithm floods one packet(called an assembly packet) throughout the network. As the packet isflooded, each node modifies it slightly to indicate what the next nodeshould do. The assembly packet tells a node whether it is a base or aremote, and to what cluster it belongs. If a node has seen an assemblypacket before, it will ignore all further assembly packets.

The algorithm starts by selecting (manually or automatically) a startnode. For example, this could be the first node to wake up. This startnode becomes a base on cluster 1, and floods an assembly packet to allof its neighbors, telling them to be remotes on cluster 1. These remotesin turn tell all their neighbors to be bases on cluster 2. Only nodesthat have not seen an assembly packet before will respond to thisrequest, so nodes that already have decided what to be will not changetheir status. The packet continues on, oscillating back and forthbetween “become base/become remote”, and increasing the cluster numbereach time. Since the packet is flooded to all neighbors at every step,it will reach every node in the network. Because of the oscillatingnature of the “become base/become remote” instructions, no two baseswill be adjacent. The basic algorithm establishes a multi-clusternetwork with all gateways between clusters, but self-assembly time isproportional with the size of the network. Further, it includes onlysingle hop clusters. Many generalizations are possible, however. If manynodes can begin the network nucleation, all that is required toharmonize the clusters is a mechanism that recognizes precedence (e.g.,time of nucleation, size of subnetwork), so that conflicts in boundaryclusters are resolved. Multiple-hop clusters can be enabled by means ofestablishing new clusters from nodes that are N hops distant from themaster.

Having established a network in this fashion, the masters can beoptimized either based on number of neighbors, or other criteria such asminimum energy per neighbor communication.

Thus, the basic algorithm is at the heart of a number of variations thatlead to a scalable multi-cluster network that establishes itself intime, and that is nearly independent of the number of nodes, withclusters arranged according to any of a wide range of optimalitycriteria. Network synchronism is established at the same time as thenetwork connections, since the assembly packet(s) convey timinginformation outwards from connected nodes.

The network nodes can be mesh network appliances to provide voicecommunications, home security, door access control, lighting control,power outlet control, dimmer control, switch control, temperaturecontrol, humidity control, carbon monoxide control, fire alarm control,blind control, shade control, window control, oven control, cookingrange control, personal computer control, entertainment console control,television control, projector control, garage door control, car control,pool temperature control, water pump control, furnace control, heatercontrol, thermostat control, electricity meter monitor, water metermonitor, gas meter monitor, or remote diagnostics. The telephone can beconnected to a cellular telephone to answer calls directed at thecellular telephone. The connection can be wired or wireless usingBluetooth or ZigBee. The telephone synchronizes calendar, contact,emails, blogs, or instant messaging with the cellular telephone.Similarly, the telephone synchronizes calendar, contact, emails, blogs,or instant messaging with a personal computer. A web server cancommunicate with the Internet through the POTS to provide information toan authorized remote user who logs into the server. A wireless routersuch as 802.11 router, 802.16 router, WiFi router, WiMAX router,Bluetooth router, X10 router can be connected to the mesh network.

A mesh network appliance can be connected to a power line to communicateX10 data to and from the mesh network. X10 is a communication protocolthat allows up to 256 X10 products to talk to each other using theexisting electrical wiring in the home. Typically, the installation issimple, a transmitter plugs (or wires) in at one location in the homeand sends its control signal (on, off, dim, bright, etc.) to a receiverwhich plugs (or wires) into another location in the home. The meshnetwork appliance translates messages intended for X10 device to berelayed over the ZigBee wireless network, and then transmitted over thepower line using a ZigBee to X10 converter appliance.

An in-door positioning system links one or more mesh network appliancesto provide location information. Inside the home or office, the radiofrequency signals have negligible multipath delay spread (for timingpurposes) over short distances. Hence, radio strength can be used as abasis for determining position. Alternatively, time of arrival can beused to determine position, or a combination of radio signal strengthand time of arrival can be used. Position estimates can also be achievedin an embodiment by beamforming, a method that exchanges time-stampedraw data among the nodes. While the processing is relatively morecostly, it yields processed data with a higher signal to noise ratio(SNR) for subsequent classification decisions, and enables estimates ofangles of arrival for targets that are outside the convex hull of theparticipating sensors. Two such clusters of ZigBee nodes can thenprovide for triangulation of distant targets. Further, beamformingenables suppression of interfering sources, by placing nulls in thesynthetic beam pattern in their directions. Another use of beamformingis in self-location of nodes when the positions of only a very smallnumber of nodes or appliances are known such as those sensors nearestthe wireless stations. In one implementation where each node knows thedistances to its neighbors due to their positions, and some smallfraction of the nodes (such as those nearest a PC with GPS) of thenetwork know their true locations. As part of the network-buildingprocedure, estimates of the locations of the nodes that lie within ornear the convex hull of the nodes with known position can be quicklygenerated. To start, the shortest distance (multihop) paths aredetermined between each reference node. All nodes on this path areassigned a location that is the simple linear average of the tworeference locations, as if the path were a straight line. A node whichlies on the intersection of two such paths is assigned the average ofthe two indicated locations. All nodes that have been assigned locationsnow serve as references. The shortest paths among these new referencenodes are computed, assigning locations to all intermediate nodes asbefore, and continuing these iterations until no further nodes getassigned locations. This will not assign initial position estimates toall sensors. The remainder can be assigned locations based on pairwiseaverages of distances to the nearest four original reference nodes. Someconsistency checks on location can be made using trigonometry and onefurther reference node to determine whether or not the node likely lieswithin the convex hull of the original four reference sensors.

In two dimensions, if two nodes have known locations, and the distancesto a third node are known from the two nodes, then trigonometry can beused to precisely determine the location of the third node. Distancesfrom another node can resolve any ambiguity. Similarly, simple geometryproduces precise calculations in three dimensions given four referencenodes. But since the references may also have uncertainty, analternative procedure is to perform a series of iterations wheresuccessive trigonometric calculations result only in a delta of movementin the position of the node. This process can determine locations ofnodes outside the convex hull of the reference sensors. It is alsoamenable to averaging over the positions of all neighbors, since therewill often be more neighbors than are strictly required to determinelocation. This will reduce the effects of distance measurement errors.Alternatively, the network can solve the complete set of equations ofintersections of hyperbola as a least squares optimization problem.

In yet another embodiment, any or all of the nodes may includetransducers for acoustic, infrared (IR), and radio frequency (RF)ranging. Therefore, the nodes have heterogeneous capabilities forranging. The heterogeneous capabilities further include differentmargins of ranging error. Furthermore, the ranging system is re-used forsensing and communication functions. For example, wideband acousticfunctionality is available for use in communicating, bistatic sensing,and ranging. Such heterogeneous capability of the sensors 40 can providefor ranging functionality in addition to communications functions. Asone example, repeated use of the communications function improvesposition determination accuracy over time. Also, when the ranging andthe timing are conducted together, they can be integrated in aself-organization protocol in order to reduce energy consumption.Moreover, information from several ranging sources is capable of beingfused to provide improved accuracy and resistance to environmentalvariability. Each ranging means is exploited as a communication means,thereby providing improved robustness in the presence of noise andinterference. Those skilled in the art will realize that there are manyarchitectural possibilities, but allowing for heterogeneity from theoutset is a component in many of the architectures.

Turning now to FIGS. 8-13, various exemplary monitoring devices areshown. In FIG. 8, a ring 130 has an opening 132 for transmitting andreceiving acoustic energy to and from the sensor 84 in an acousticimplementation. In an optical implementation, a second opening (notshown) is provided to emit an optical signal from an LED, for example,and an optical detector can be located at the opening 132 to receive theoptical signal passing through the finger wearing the ring 130. Inanother implementation, the ring has an electrically movable portion 134and rigid portions 136-138 connected thereto. The electrically movableportion 134 can squeeze the finger as directed by the CPU during anapplanation sweep to determine the arterial blood pressure.

FIG. 9 shows an alternate finger cover embodiment where a finger-mountedmodule housing the photo-detector and light source. The finger mountedmodule can be used to measure information that is processed to determinethe user's blood pressure by measuring blood flow in the user's fingerand sending the information through a wireless connection to the basestation. In one implementation, the housing is made from a flexiblepolymer material.

In an embodiment to be worn on the patient's ear lobe, the monitoringdevice can be part of an earring jewelry clipped to the ear lobe. In theimplementation of FIG. 10, the monitoring device has a jewelry body 149that contains the monitoring electronics and power source. The surfaceof the body 149 is an ornamental surface such as jade, ivory, pearl,silver, or gold, among others. The body 149 has an opening 148 thattransmits energy such as optical or acoustic energy through the ear lobeto be detected by a sensor 144 mounted on a clamp portion that issecured to the body 149 at a base 147. The energy detected through thesensor 144 is communicated through an electrical connector to theelectronics in the jewelry body 149 for processing the received energyand for performing wireless communication with a base station. In FIG.2E, a bolt 145 having a stop end 146 allows the user to adjust thepressure of the clamp against the ear lobe. In other implementations, aspring biased clip is employed to retain the clip on the wearer's earlobe. A pair of members, which snap together under pressure, arecommonly used and the spring pressure employed should be strong enoughto suit different thicknesses of the ear lobe.

FIGS. 11 and 12 show two additional embodiments of the monitoringdevice. In FIG. 11, a wearable monitoring device is shown. Themonitoring device has a body 160 comprising microphone ports 162, 164and 170 arranged in a first order noise cancelling microphonearrangement. The microphones 162 and 164 are configured to optimallyreceive distant noises, while the microphone 170 is optimized forcapturing the user's speech. A touch sensitive display 166 and aplurality of keys 168 are provided to capture hand inputs. Further, aspeaker 172 is provided to generate a verbal feedback to the user.

Turning now to FIG. 12, a jewelry-sized monitoring device isillustrated. In this embodiment, a body 172 houses a microphone port 174and a speaker port 176. The body 172 is coupled to the user via thenecklace 178 so as to provide a personal, highly accessible personalcomputer. Due to space limitations, voice input/output is an importantuser interface of the jewelry-sized computer. Although a necklace isdisclosed, one skilled in the art can use a number of other substitutessuch as a belt, a brace, a ring, or a band to secure the jewelry-sizedcomputer to the user.

FIG. 13 shows an exemplary ear phone embodiment 180. The ear phone 180has an optical transmitter 182 which emits LED wavelengths that arereceived by the optical receiver 184. The blood oximetry information isgenerated and used to determine blood pulse or blood pressure.Additionally, a module 186 contains mesh network communicationelectronics, accelerometer, and physiological sensors such as EKG/ECGsensors or temperature sensors or ultrasonic sensors. In addition, aspeaker (not shown) is provided to enable voice communication over themesh network, and a microphone 188 is provided to pick up voice duringverbal communication and pick up heart sound when the user is not usingthe microphone for voice communication. The ear phone optionally has anear canal temperature sensor for sensing temperature in a human.

FIG. 14A shows an exemplary adhesive patch embodiment. The patch may beapplied to a persons skin by anyone including the person themselves oran authorized person such as a family member or physician. The adhesivepatch is shown generally at 190 having a gauze pad 194 attached to oneside of a backing 192, preferably of plastic, and wherein the pad canhave an impermeable side 194 coating with backing 192 and a module 196which contains electronics for communicating with the mesh network andfor sensing acceleration and bioimpedance, EKG/ECG, heart sound,microphone, optical sensor, or ultrasonic sensor in contacts with awearer's skin. In one embodiment, the module 196 has a skin side thatmay be coated with a conductive electrode lotion or gel to improve thecontact. The entire patch described above may be covered with a plasticor foil strip to retain moisture and retard evaporation by a conductiveelectrode lotion or gel provided improve the electrode contact. In oneembodiment, an acoustic sensor (microphone or piezoelectric sensor) andan electrical sensor such as EKG sensor contact the patient with aconductive gel material. The conductive gel material providestransmission characteristics so as to provide an effective acousticimpedance match to the skin in addition to providing electricalconductivity for the electrical sensor. The acoustic transducer can bedirected mounted on the conductive gel material substantially with orwithout an intermediate air buffer. The entire patch is then packaged assterile as are other over-the-counter adhesive bandages. When the patchis worn out, the module 196 may be removed and a new patch backing 192may be used in place of the old patch. One or more patches may beapplied to the patient's body and these patches may communicatewirelessly using the mesh network or alternatively they may communicatethrough a personal area network using the patient's body as acommunication medium.

The term “positional measurement,” as that term is used herein, is notlimited to longitude and latitude measurements, or to metes and bounds,but includes information in any form from which geophysical positionscan be derived. These include, but are not limited to, the distance anddirection from a known benchmark, measurements of the time required forcertain signals to travel from a known source to the geophysicallocation where the signals may be electromagnetic or other forms, ormeasured in terms of phase, range, Doppler or other units.

FIG. 14B shows a sunglass or eyeglass embodiment which containselectronics for communicating with the mesh network and for sensingacceleration and bioimpedance, EKG/ECG, EMG, heart sound, microphone,optical sensor, or ultrasonic sensor in contacts with a wearer's skin.In one embodiment, the ear module 310 contains optical sensors to detecttemperature, blood flow and blood oxygen level as well as a speaker toprovide wireless communication or hearing aid. The blood flow orvelocity information can be used to estimate blood pressure. The sidemodule 312 can contain an array of bioimpedance sensors such as bipolaror tetrapolar bioimpedance probes to sense fluids in the brain.Additional bioimpedance electrodes can be positioned around the rim ofthe glasses as well as the glass handle or in any spots on the eyewearthat contacts the user. The side module 312 or 314 can also contain oneor more EKG electrodes to detect heart beat parameters and to detectheart problems. The side module 312 or 314 can also containpiezoelectric transducers or microphones to detect heart activities nearthe brain. The side module 312 or 314 can also contain ultrasoundtransmitter and receiver to create an ultrasound model of brain fluids.In one embodiment, an acoustic sensor (microphone or piezoelectricsensor) and an electrical sensor such as EKG sensor contact the patientwith a conductive gel material. The conductive gel material providestransmission characteristics so as to provide an effective acousticimpedance match to the skin in addition to providing electricalconductivity for the electrical sensor. The acoustic transducer can bedirected mounted on the conductive gel material substantially with orwithout an intermediate air buffer. In another embodiment, electronicscomponents are distributed between first and second ear stems. In yetanother embodiment, the method further comprises providing a nosebridge, wherein digital signals generated by the electronics circuit aretransmitted across the nose bridge. The eyewear device may communicatewirelessly using the mesh network or alternatively they may communicatethrough a personal area network using the patient's body as acommunication medium. Voice can be transmitted over the mesh wirelessnetwork. The speaker can play digital audio file, which can becompressed according to a compression format. The compression format maybe selected from the group consisting of: PCM, DPCM, ADPCM, AAC, RAW,DM, RIFF, WAV, BWF, AIFF, AU, SND, CDA, MPEG, MPEG-1, MPEG-2, MPEG-2.5,MPEG-4, MPEG-J, MPEG 2-ACC, MP3, MP3Pro, ACE, MACE, MACE-3, MACE-6,AC-3, ATRAC, ATRAC3, EPAC, Twin VQ, VQF, WMA, WMA with DRM, DTS, DVDAudio, SACD, TAC, SHN, OGG, Ogg Vorbis, Ogg Tarkin, Ogg Theora, ASF,LQT, QDMC, A2b, .ra, .rm, and Real Audio G2, RMX formats, Fairplay,Quicktime, SWF, and PCA, among others.

In one embodiment, the eye wear device of FIG. 14B can provide a dataport, wherein the data port is carried by the ear stem. The data portmay be a mini-USB connector, a FIREWIRE connector, an IEEE 1394 cableconnector, an RS232 connector, a JTAB connector, an antenna, a wirelessreceiver, a radio, an RF receiver, or a Bluetooth receiver. In anotherembodiment, the wearable device is removably connectable to a computingdevice. The wearable wireless audio device may be removably connectableto a computing device with a data port, wherein said data port ismounted to said wearable wireless audio device. In another embodiment,projectors can project images on the glasses to provide head-mounteddisplay on the eye wear device. The processor can display fact, figure,to do list, and reminders need in front of the user's eyes.

FIG. 15A shows a system block diagram of the network-based patientmonitoring system in a hospital or nursing home setting. The system hasa patient component 215, a server component 216, and a client component217. The patient component 215 has one or more mesh network patienttransmitters 202 for transmitting data to the central station. Thecentral server comprises one or more Web servers 205, one or morewaveform servers 204 and one or more mesh network receivers 211. Theoutput of each mesh network receiver 211 is connected to at least one ofthe waveform servers 204. The waveform servers 204 and Web the servers205 are connected to the network 105. The Web servers 205 are alsoconnected to a hospital database 230. The hospital database 230 containspatient records. In the embodiment of FIG. 15A, a plurality of nursestations provide a plurality of nurse computer user interface 208. Theuser interface 208 receives data from an applet 210 that communicateswith the waveform server 204 and updates the display of the nursecomputers for treating patients.

The network client component 217 comprises a series of workstations 106connected to the network 105. Each workstation 106 runs a World Wide Web(WWW or Web) browser application 208. Each Web browser can open a pagethat includes one or more media player applets 210. The waveform servers204 use the network 105 to send a series of messages 220 to the Webservers 205. The Web servers 205 use the network 105 to communicatemessages, shown as a path 221, to the workstations 106. The media playerapplets running on the workstations 106 use the network 105 to sendmessages over a path 223 directly to the waveform servers 204.

FIG. 15B shows a variation of the system of FIG. 15A for call centermonitoring. In this embodiment, the patient appliances 202 wirelesslycommunicate to home base stations (not shown) which are connected to thePOTS or PSTN network for voice as well as data transmission. The data iscaptured by the waveform server 204 and the voice is passed through tothe call center agent computer 207 where the agent can communicate byvoice with the patient. The call center agent can forward the call to aprofessional such as a nurse or doctor or emergency service personnel ifnecessary. Hence, the system can include a patient monitoring appliancecoupled to the POTS or PSTN through the mesh network. The patientmonitoring appliance monitors drug usage and patient falls. The patientmonitoring appliance monitors patient movement. A call center can callto the telephone to provide a human response.

In one exemplary monitoring service providing system, such as anemergency service providing system, the system includes a communicationnetwork (e.g., the Public Switch Telephone Network or PSTN or POTS), awide area communication network (e.g., TCP/IP network) in call centers.The communication network receives calls destined for one of the callcenters. In this regard, each call destined for one of the call centersis preferably associated with a particular patient, a call identifier ora call identifier of a particular set of identifiers. A call identifierassociated with an incoming call may be an identifier dialed orotherwise input by the caller. For example, the call centers may belocations for receiving calls from a particular hospital or nursinghome.

To network may analyze the automatic number information (ANI) and/orautomatic location information (ALI) associated with the call. In thisregard, well known techniques exist for analyzing the ANI and ALI of anincoming call to identify the call as originating from a particularcalling device or a particular calling area. Such techniques may beemployed by the network to determine whether an incoming call originatedfrom a calling device within an area serviced by the call centers.Moreover, if an incoming call originated from such an area and if theincoming call is associated with the particular call identifier referredto above, then the network preferably routes the call to a designatedfacility.

When a call is routed to the facility, a central data manager, which maybe implemented in software, hardware, or a combination thereof,processes the call according to techniques that will be described inmore detail hereafter and routes the call, over the wide area network,to one of the call centers depending on the ANI and/or ALI associatedwith the call. In processing the call, the central data manager mayconvert the call from one communication protocol to anothercommunication protocol, such as voice over internet protocol (VoIP), forexample, in order to increase the performance and/or efficiency of thesystem. The central data manager may also gather information to help thecall centers in processing the call. There are various techniques thatmay be employed by the central data manager to enhance the performanceand/or efficiency of the system, and examples of such techniques will bedescribed in more detail hereafter.

Various benefits may be realized by utilizing a central facility tointercept or otherwise receive a call from the network and to then routethe call to one of the call centers via WAN. For example, servingmultiple call centers with a central data manager, may help to reducetotal equipment costs. In this regard, it is not generally necessary toduplicate the processing performed by the central data manager at eachof the call centers. Thus, equipment at each of the call centers may bereduced. As more call centers are added, the equipment savings enabledby implementing equipment at the central data manager instead of thecall centers generally increases. Furthermore, the system is notdependent on any telephone company's switch for controlling the mannerin which data is communicated to the call centers. In this regard, thecentral data manager may receive a call from the network and communicatethe call to the destination call centers via any desirable communicationtechnique, such as VoIP, for example. Data security is another possiblebenefit of the exemplary system 10 as the central data manager is ableto store the data for different network providers associated withnetwork on different partitions.

While the patient interface 90 (FIG. 1A) can provide information for asingle person, FIG. 15C shows an exemplary interface to monitor aplurality of persons, while FIG. 15D shows an exemplary dash-board thatprovides summary information on the status of a plurality of persons. Asshown in FIG. 1C, for professional use such as in hospitals, nursinghomes, or retirement homes, a display can track a plurality of patients.In FIG. 15C, a warning (such as sound or visual warning in the form oflight or red flashing text) can be generated to point out the particularpatient that may need help or attention. In FIG. 15D, a magnifier glasscan be dragged over a particular individual icon to expand and showdetailed vital parameters of the individual and if available, imagesfrom the camera 10 trained on the individual for real time videofeedback. The user can initiate voice communication with the user forconfirmation purposes by clicking on a button provided on the interfaceand speaking into a microphone on the professional's workstation.

In one embodiment for professional users such as hospitals and nursinghomes, a Central Monitoring Station provides alarm and vital signoversight for a plurality of patients from a single computerworkstation. FIG. 15E shows an exemplary multi-station vital parameteruser interface for a professional embodiment, while FIG. 15F shows anexemplary trending pattern display. The clinician interface uses simplepoint and click actions with a computer mouse or trackball. Theclinician can initiate or change monitoring functions from either theCentral Station or the bedside monitor. One skilled in the art willrecognize that patient data such as EKG/EMG/EEG/BP data can be showneither by a scrolling waveform that moves along the screen display, orby a moving bar where the waveform is essentially stationary and the barmoves across the screen.

In one embodiment, software for the professional monitoring systemprovides a login screen to enter user name and password, together withdatabase credentials. In Select Record function, the user can select aperson, based on either entered or pre-selected criteria. From herenavigate to their demographics, medical record, etc. The system can showa persons demographics, includes aliases, people involved in their care,friends and family, previous addresses, home and work locations,alternative numbers and custom fields. The system can show all dataelements of a person's medical record. These data elements are not ‘hardwired’, but may be configured in the data dictionary to suit particularuser requirements. It is possible to create views of the record thatfilter it to show (for instance) just the medications or diagnosis, etc.Any data element can be designated ‘plan able’ in the data dictionaryand then scheduled. A Summary Report can be done. Example of a reportdisplayed in simple format, selecting particular elements and dates. Asmany of these reports as required can be created, going across all datain the system based on some criteria, with a particular selection offields and sorting, grouping and totaling criteria. Reports can becreated that can format and analyze any data stored on the server. Thesystem supports OLE controls and can include graphs, bar codes, etc.These can be previewed on screen, printed out or exported in a widevariety of formats. The system also maintains a directory of allorganizations the administrator wishes to record as well as your own.These locations are then used to record the location for elements of themedical record (where applicable), work addresses for people involved inthe care and for residential addresses for people in residential care.The data elements that form the medical record are not ‘hard wired’ (iepredefined) but may be customized by the users to suit current andfuture requirements.

In one embodiment, the wearable appliance can store patient data in itsdata storage device such as flash memory. The data can includeImmunizations and dates; medications (prescriptions and supplements);physician names, addresses, phone numbers, email addresses; location anddetails of advance directives; insurance company, billing address, phonenumber, policy number; emergency contacts, addresses,home/business/pager phone numbers, email addresses. The data can includecolor or black and white photo of the wearer of the device; a thumbprint, iris print of other distinguishing physical characteristic;dental records; sample ECG or Cardiac Echo Scan.; blood type; presentmedication being taken; drug interaction precautions; drug and/orallergic reaction precautions; a description of serious preexistingmedical conditions; Emergency Medical Instructions, which could include:administering of certain suggested drugs or physical treatments; callingemergency physician numbers listed; bringing the patient to a certaintype of clinic or facility based on religious beliefs; and living willinstructions in the case of seriously ill patients; Organ Donorinstructions; Living Will instructions which could include: instructionsfor life support or termination of treatment; notification of next ofkin and/or friends including addresses and telephone numbers; ECG trace;Cardiac Echo Scan; EEG trace; diabetes test results; x-ray scans, amongothers. The wearable appliance stores the wearer's medical records andID information. In one embodiment, to start the process new/originalmedical information is organized and edited to fit into the BWD pageformat either in physicians office or by a third party with access to apatient's medical records using the base unit storage and encryptingsoftware which can be stored in a normal pc or other compatible computerdevice. The system can encrypt the records so as to be secure andconfidential and only accessible to authorized individuals withcompatible de-encrypting software. In the event the wearer is strickenwith an emergency illness a Paramedic, EMT or Emergency Room Techniciancan use a wireless interrogator to rapidly retrieve and display thestored medical records in the wearable appliance and send the medicalrecords via wireless telemetry to a remote emergency room or physiciansoffice for rapid and life saving medical intervention in a crisissituation. In a Non-emergency Situation, the personal health informationservice is also helpful as it eliminates the hassle of repeatedlyfilling out forms when changing health plans or seeing a new physician;stores vaccination records to schools or organizations without callingthe pediatrician; or enlists the doctor's or pharmacist's advice aboutmultiple medications without carrying all the bottles to a personalvisit. The system can store 48 hrs. of EKG, EEG, EMG, or blood pressuredata.

In one embodiment, a plurality of body worn sensors with in-doorpositioning can be used as an Emergency Department and Urgent CareCenter Tracking System. The system tracks time from triage to MDassessment, identifies patients that have not yet been registered,records room usage, average wait time, and average length of stay. Thesystem allows user defined “activities” so that hospitals can tracktimes and assist in improving patient flow and satisfaction. The systemcan set custom alerts and send email/pager notifications to betteridentify long patient wait times and record the number of these alertoccurrences. The system can manage room usage by identifying those roomswhich are under/over utilized. The hospital administrator can set manualor automatic alerts and generate custom reports for analysis of patientflow. The system maximizes revenue by streamlining processes andimproving throughput; improves charge capture by ensuring compliancewith regulatory standards; increases accountability by collecting clear,meaningful data; enhances risk management and QA; and decreasesliability.

FIG. 16A shows ant exemplary process to continuously determine bloodpressure of a patient. The process generates a blood pressure model of apatient (2002); determines a blood flow velocity using a piezoelectrictransducer (2004); and provides the blood flow velocity to the bloodpressure model to continuously estimate blood pressure (2006).

FIG. 16B shows another exemplary process to continuously determine bloodpressure of a patient. First, during an initialization mode, amonitoring device and calibration device are attached to patient (2010).The monitoring device generates patient blood flow velocity, whileactual blood pressure is measured by a calibration device (2012). Next,the process generates a blood pressure model based on the blood flowvelocity and the actual blood pressure (2014). Once this is done, thecalibration device can be removed (2016). Next, during an operationmode, the process periodically samples blood flow velocity from themonitoring device on a real-time basis (18) and provides the blood flowvelocity as input information to the blood pressure model to estimateblood pressure (20). This process can be done in continuously orperiodically as specified by a user.

In one embodiment, to determine blood flow velocity, acoustic pulses aregenerated and transmitted into the artery using an ultrasonic transducerpositioned near a wrist artery. These pulses are reflected by variousstructures or entities within the artery (such as the artery walls, andthe red blood cells within the subject's blood), and subsequentlyreceived as frequency shifts by the ultrasonic transducer. Next, theblood flow velocity is determined. In this process, the frequencies ofthose echoes reflected by blood cells within the blood flowing in theartery differ from that of the transmitted acoustic pulses due to themotion of the blood cells. This well known “Doppler shift” in frequencyis used to calculate the blood flow velocity. In one embodiment fordetermining blood flow velocity, the Doppler frequency is used todetermine mean blood velocity. For example, U.S. Pat. No. 6,514,211, thecontent of which is incorporated by reference, discusses blood flowvelocity using a time-frequency representation.

In one implementation, the system can obtain one or more numericalcalibration curves describing the patient's vital signs such as bloodpressure. The system can then direct energy such as infrared orultrasound at the patient's artery and detecting reflections thereof todetermine blood flow velocity from the detected reflections. The systemcan numerically fit or map the blood flow velocity to one or morecalibration parameters describing a vital-sign value. The calibrationparameters can then be compared with one or more numerical calibrationcurves to determine the blood pressure.

Additionally, the system can analyze blood pressure, and heart rate, andpulse oximetry values to characterize the user's cardiac condition.These programs, for example, may provide a report that featuresstatistical analysis of these data to determine averages, data displayedin a graphical format, trends, and comparisons to doctor-recommendedvalues.

In one embodiment, feed forward artificial neural networks (NNs) areused to classify valve-related heart disorders. The heart sounds arecaptured using the microphone or piezoelectric transducer. Relevantfeatures were extracted using several signal processing tools, discretewavelet transfer, fast fourier transform, and linear prediction coding.The heart beat sounds are processed to extract the necessary featuresby: a) denoising using wavelet analysis, b) separating one beat out ofeach record c) identifying each of the first heart sound (FHS) and thesecond heart sound (SHS). Valve problems are classified according to thetime separation between the FHS and th SHS relative to cardiac cycletime, namely whether it is greater or smaller than 20% of cardiac cycletime. In one embodiment, the NN comprises 6 nodes at both ends, with onehidden layer containing 10 nodes. In another embodiment, linearpredictive code (LPC) coefficients for each event were fed to twoseparate neural networks containing hidden neurons.

In another embodiment, a normalized energy spectrum of the sound data isobtained by applying a Fast Fourier Transform. The various spectralresolutions and frequency ranges were used as inputs into the NN tooptimize these parameters to obtain the most favorable results.

In another embodiment, the heart sounds are denoised using six-stagewavelet decomposition, thresholding, and then reconstruction. Threefeature extraction techniques were used: the Decimation method, and thewavelet method. Classification of the heart diseases is done usingHidden Markov Models (HMMs).

In yet another embodiment, a wavelet transform is applied to a window oftwo periods of heart sounds. Two analyses are realized for the signalsin the window: segmentation of first and second heart sounds, and theextraction of the features. After segmentation, feature vectors areformed by using he wavelet detail coefficients at the sixthdecomposition level. The best feature elements are analyzed by usingdynamic programming.

In another embodiment, the wavelet decomposition and reconstructionmethod extract features from the heart sound recordings. An artificialneural network classification method classifies the heart sound signalsinto physiological and pathological murmurs. The heart sounds aresegmented into four parts: the first heart sound, the systolic period,the second heart sound, and the diastolic period. The following featurescan be extracted and used in the classification algorithm: a) Peakintensity, peak timing, and the duration of the first heart sound b) theduration of the second heart sound c) peak intensity of the aorticcomponent of S2(A2) and the pulmonic component of S2 (P2), the splittinginterval and the reverse flag of A2 and P2, and the timing of A2 d) theduration, the three largest frequency components of the systolic signaland the shape of the envelope of systolic murmur e) the duration thethree largest frequency components of the diastolic signal and the shapeof the envelope of the diastolic murmur.

In one embodiment, the time intervals between the ECG R-waves aredetected using an envelope detection process. The intervals between Rand T waves are also determined. The Fourier transform is applied to thesound to detect S1 and S2. To expedite processing, the system appliesFourier transform to detect S1 in the interval 0.1-0.5 R-R. The systemlooks for S2 the intervals R-T and 0.6 R-R. S2 has an aortic componentA2 and a pulmonary component P2. The interval between these twocomponents and its changes with respiration has clinical significance.A2 sound occurs before P2, and the intensity of each component dependson the closing pressure and hence A2 is louder than P2. The third heardsound S3 results from the sudden halt in the movement of the ventriclein response to filling in early diastole after the AV valves and isnormally observed in children and young adults. The fourth heart soundS4 is caused by the sudden halt of the ventricle in response to fillingin presystole due to atrial contraction.

In yet another embodiment, the S2 is identified and a normalizedsplitting interval between A2 and P2 is determined. If there is nooverlap, A2 and P2 are determined from the heart sound. When overlapexists between A2 and P2, the sound is dechirped for identification andextraction of A2 and P2 from S2. The A2-P2 splitting interval (SI) iscalculated by computing the cross-correlation function between A2 and P2and measuring the time of occurrence of its maximum amplitude. SI isthen normalized (NSI) for heart rate as follows: NSI=SI/cardiac cycletime. The duration of the cardiac cycle can be the average interval ofQRS waves of the ECG. It could also be estimated by computing the meaninterval between a series of consecutive S1 and S2 from the heart sounddata. A non linear regressive analysis maps the relationship between thenormalized NSI and PAP. A mapping process such as a curve-fittingprocedure determines the curve that provides the best fit with thepatient data. Once the mathematical relationship is determined, NSI canbe used to provide an accurate quantitative estimate of the systolic andmean PAP relatively independent of heart rate and systemic arterialpressure.

In another embodiment, the first heart sound (S1) is detected using atime-delayed neural network (TDNN). The network consists of a singlehidden layer, with time-delayed links connecting the hidden units to thetime-frequency energy coefficients of a Morlet wavelet decomposition ofthe input phonocardiogram (PCG) signal. The neural network operates on a200 msec sliding window with each time-delay hidden unit spanning 100msec of wavelet data.

In yet another embodiment, a local signal analysis is used with aclassifier to detect, characterize, and interpret sounds correspondingto symptoms important for cardiac diagnosis. The system detects aplurality of different heart conditions. Heart sounds are automaticallysegmented into a segment of a single heart beat cycle. Each segment arethen transformed using 7 level wavelet decomposition, based on Coifman4th order wavelet kernel. The resulting vectors 4096 values, are reducedto 256 element feature vectors, this simplified the neural network andreduced noise.

In another embodiment, feature vectors are formed by using the waveletdetail and approximation coefficients at the second and sixthdecomposition levels. The classification (decision making) is performedin 4 steps: segmentation of the first and second heart sounds,normalization process, feature extraction, and classification by theartificial neural network.

In another embodiment using decision trees, the system distinguishes (1)the Aortic Stenosis (AS) from the Mitral Regurgitation (MR) and (2) theOpening Snap (OS), the Second Heart Sound Split (A2_P2) and the ThirdHeart Sound (S3). The heart sound signals are processed to detect thefirst and second heart sounds in the following steps: a) waveletdecomposition, b) calculation of normalized average Shannon Energy, c) amorphological transform action that amplifies the sharp peaks andattenuates the broad ones d) a method that selects and recovers thepeaks corresponding to S1 and S2 and rejects others e) algorithm thatdetermines the boundaries of S1 and S2 in each heart cycle f) a methodthat distinguishes S1 from S2.

In one embodiment, once the heart sound signal has been digitized andcaptured into the memory, the digitized heart sound signal isparameterized into acoustic features by a feature extractor. The outputof the feature extractor is delivered to a sound recognizer. The featureextractor can include the short time energy, the zero crossing rates,the level crossing rates, the filter-bank spectrum, the linearpredictive coding (LPC), and the fractal method of analysis. Inaddition, vector quantization may be utilized in combination with anyrepresentation techniques. Further, one skilled in the art may use anauditory signal-processing model in place of the spectral models toenhance the system's robustness to noise and reverberation.

In one embodiment of the feature extractor, the digitized heart soundsignal series s(n) is put through a low-order filter, typically afirst-order finite impulse response filter, to spectrally flatten thesignal and to make the signal less susceptible to finite precisioneffects encountered later in the signal processing. The signal ispre-emphasized preferably using a fixed pre-emphasis network, orpreemphasizer. The signal can also be passed through a slowly adaptivepre-emphasizer. The preemphasized heart sound signal is next presentedto a frame blocker to be blocked into frames of N samples with adjacentframes being separated by M samples. In one implementation, frame 1contains the first 400 samples. The frame 2 also contains 400 samples,but begins at the 300th sample and continues until the 700th sample.Because the adjacent frames overlap, the resulting LPC spectral analysiswill be correlated from frame to frame. Each frame is windowed tominimize signal discontinuities at the beginning and end of each frame.The windower tapers the signal to zero at the beginning and end of eachframe. Preferably, the window used for the autocorrelation method of LPCis the Hamming window. A noise canceller operates in conjunction withthe autocorrelator to minimize noise. Noise in the heart sound patternis estimated during quiet periods, and the temporally stationary noisesources are damped by means of spectral subtraction, where theautocorrelation of a clean heart sound signal is obtained by subtractingthe autocorrelation of noise from that of corrupted heart sound. In thenoise cancellation unit, if the energy of the current frame exceeds areference threshold level, the heart is generating sound and theautocorrelation of coefficients representing noise is not updated.However, if the energy of the current frame is below the referencethreshold level, the effect of noise on the correlation coefficients issubtracted off in the spectral domain. The result is half-wave rectifiedwith proper threshold setting and then converted to the desiredautocorrelation coefficients. The output of the autocorrelator and thenoise canceller are presented to one or more parameterization units,including an LPC parameter unit, an FFT parameter unit, an auditorymodel parameter unit, a fractal parameter unit, or a wavelet parameterunit, among others. The LPC parameter is then converted into cepstralcoefficients. The cepstral coefficients are the coefficients of theFourier transform representation of the log magnitude spectrum. A filterbank spectral analysis, which uses the short-time Fourier transformation(STFT) may also be used alone or in conjunction with other parameterblocks. FFT is well known in the art of digital signal processing. Sucha transform converts a time domain signal, measured as amplitude overtime, into a frequency domain spectrum, which expresses the frequencycontent of the time domain signal as a number of different frequencybands. The FFT thus produces a vector of values corresponding to theenergy amplitude in each of the frequency bands. The FFT converts theenergy amplitude values into a logarithmic value which reducessubsequent computation since the logarithmic values are more simple toperform calculations on than the longer linear energy amplitude valuesproduced by the FFT, while representing the same dynamic range. Ways forimproving logarithmic conversions are well known in the art, one of thesimplest being use of a look-up table. In addition, the FFT modifies itsoutput to simplify computations based on the amplitude of a given frame.This modification is made by deriving an average value of the logarithmsof the amplitudes for all bands. This average value is then subtractedfrom each of a predetermined group of logarithms, representative of apredetermined group of frequencies. The predetermined group consists ofthe logarithmic values, representing each of the frequency bands. Thus,utterances are converted from acoustic data to a sequence of vectors ofk dimensions, each sequence of vectors identified as an acoustic frame,each frame represents a portion of the utterance. Alternatively,auditory modeling parameter unit can be used alone or in conjunctionwith others to improve the parameterization of heart sound signals innoisy and reverberant environments. In this approach, the filteringsection may be represented by a plurality of filters equally spaced on alog-frequency scale from 0 Hz to about 3000 Hz and having a prescribedresponse corresponding to the cochlea. The nerve fiber firing mechanismis simulated by a multilevel crossing detector at the output of eachcochlear filter. The ensemble of the multilevel crossing intervalscorresponds to the firing activity at the auditory nerve fiber-array.The interval between each successive pair of same direction, eitherpositive or negative going, crossings of each predetermined soundintensity level is determined and a count of the inverse of theseinterspike intervals of the multilevel detectors for each spectralportion is stored as a function of frequency. The resulting histogram ofthe ensemble of inverse interspike intervals forms a spectral patternthat is representative of the spectral distribution of the auditoryneural response to the input sound and is relatively insensitive tonoise. The use of a plurality of logarithmically related sound intensitylevels accounts for the intensity of the input signal in a particularfrequency range. Thus, a signal of a particular frequency having highintensity peaks results in a much larger count for that frequency than alow intensity signal of the same frequency. The multiple levelhistograms of the type described herein readily indicate the intensitylevels of the nerve firing spectral distribution and cancel noiseeffects in the individual intensity level histograms. Alternatively, thefractal parameter block can further be used alone or in conjunction withothers to represent spectral information. Fractals have the property ofself similarity as the spatial scale is changed over many orders ofmagnitude. A fractal function includes both the basic form inherent in ashape and the statistical or random properties of the replacement ofthat shape in space. As is known in the art, a fractal generator employsmathematical operations known as local affine transformations. Thesetransformations are employed in the process of encoding digital datarepresenting spectral data. The encoded output constitutes a “fractaltransform” of the spectral data and consists of coefficients of theaffine transformations. Different fractal transforms correspond todifferent images or sounds.

Alternatively, a wavelet parameterization block can be used alone or inconjunction with others to generate the parameters. Like the FFT, thediscrete wavelet transform (DWT) can be viewed as a rotation in functionspace, from the input space, or time domain, to a different domain. TheDWT consists of applying a wavelet coefficient matrix hierarchically,first to the full data vector of length N, then to a smooth vector oflength N/2, then to the smooth-smooth vector of length N/4, and so on.Most of the usefulness of wavelets rests on the fact that wavelettransforms can usefully be severely truncated, or turned into sparseexpansions. In the DWT parameterization block, the wavelet transform ofthe heart sound signal is performed. The wavelet coefficients areallocated in a non-uniform, optimized manner. In general, large waveletcoefficients are quantized accurately, while small coefficients arequantized coarsely or even truncated completely to achieve theparameterization. Due to the sensitivity of the low-order cepstralcoefficients to the overall spectral slope and the sensitivity of thehigh-order cepstral coefficients to noise variations, the parametersgenerated may be weighted by a parameter weighing block, which is atapered window, so as to minimize these sensitivities. Next, a temporalderivator measures the dynamic changes in the spectra. Power featuresare also generated to enable the system to distinguish heart sound fromsilence.

After the feature extraction has been performed, the heart soundparameters are next assembled into a multidimensional vector and a largecollection of such feature signal vectors can be used to generate a muchsmaller set of vector quantized (VQ) feature signals by a vectorquantizer that cover the range of the larger collection. In addition toreducing the storage space, the VQ representation simplifies thecomputation for determining the similarity of spectral analysis vectorsand reduces the similarity computation to a look-up table ofsimilarities between pairs of codebook vectors. To reduce thequantization error and to increase the dynamic range and the precisionof the vector quantizer, the preferred embodiment partitions the featureparameters into separate codebooks, preferably three. In the preferredembodiment, the first, second and third codebooks correspond to thecepstral coefficients, the differenced cepstral coefficients, and thedifferenced power coefficients.

With conventional vector quantization, an input vector is represented bythe codeword closest to the input vector in terms of distortion. Inconventional set theory, an object either belongs to or does not belongto a set. This is in contrast to fuzzy sets where the membership of anobject to a set is not so clearly defined so that the object can be apart member of a set. Data are assigned to fuzzy sets based upon thedegree of membership therein, which ranges from 0 (no membership) to 1.0(full membership). A fuzzy set theory uses membership functions todetermine the fuzzy set or sets to which a particular data value belongsand its degree of membership therein.

To handle the variance of heart sound patterns of individuals over timeand to perform speaker adaptation in an automatic, self-organizingmanner, an adaptive clustering technique called hierarchical spectralclustering is used. Such speaker changes can result from temporary orpermanent changes in vocal tract characteristics or from environmentaleffects. Thus, the codebook performance is improved by collecting heartsound patterns over a long period of time to account for naturalvariations in speaker behavior. In one embodiment, data from the vectorquantizer is presented to one or more recognition models, including anHMM model, a dynamic time warping model, a neural network, a fuzzylogic, or a template matcher, among others. These models may be usedsingly or in combination.

In dynamic processing, at the time of recognition, dynamic programmingslides, or expands and contracts, an operating region, or window,relative to the frames of heart sound so as to align those frames withthe node models of each S1-S4 pattern to find a relatively optimal timealignment between those frames and those nodes. The dynamic processingin effect calculates the probability that a given sequence of framesmatches a given word model as a function of how well each such framematches the node model with which it has been time-aligned. The wordmodel which has the highest probability score is selected ascorresponding to the heart sound.

Dynamic programming obtains a relatively optimal time alignment betweenthe heart sound to be recognized and the nodes of each word model, whichcompensates for the unavoidable differences in speaking rates whichoccur in different utterances of the same word. In addition, sincedynamic programming scores words as a function of the fit between wordmodels and the heart sound over many frames, it usually gives thecorrect word the best score, even if the word has been slightlymisspoken or obscured by background sound. This is important, becausehumans often mispronounce words either by deleting or mispronouncingproper sounds, or by inserting sounds which do not belong.

In dynamic time warping (DTW), the input heart sound A, defined as thesampled time values A=a(1) . . . a(n), and the vocabulary candidate B,defined as the sampled time values B=b(1) . . . b(n), are matched up tominimize the discrepancy in each matched pair of samples. Computing thewarping function can be viewed as the process of finding the minimumcost path from the beginning to the end of the words, where the cost isa function of the discrepancy between the corresponding points of thetwo words to be compared. Dynamic programming considers all possiblepoints within the permitted domain for each value of i. Because the bestpath from the current point to the next point is independent of whathappens beyond that point. Thus, the total cost of [i(k), j(k)] is thecost of the point itself plus the cost of the minimum path to it.Preferably, the values of the predecessors can be kept in an M×N array,and the accumulated cost kept in a 2×N array to contain the accumulatedcosts of the immediately preceding column and the current column.However, this method requires significant computing resources. For theheart sound recognizer to find the optimal time alignment between asequence of frames and a sequence of node models, it must compare mostframes against a plurality of node models. One method of reducing theamount of computation required for dynamic programming is to use pruningPruning terminates the dynamic programming of a given portion of heartsound against a given word model if the partial probability score forthat comparison drops below a given threshold. This greatly reducescomputation, since the dynamic programming of a given portion of heartsound against most words produces poor dynamic programming scores ratherquickly, enabling most words to be pruned after only a small percent oftheir comparison has been performed. To reduce the computationsinvolved, one embodiment limits the search to that within a legal pathof the warping.

A Hidden Markov model can be used in one embodiment to evaluate theprobability of occurrence of a sequence of observations O(1), O(2), . .. O(t), . . . , O(T), where each observation O(t) may be either adiscrete symbol under the VQ approach or a continuous vector. Thesequence of observations may be modeled as a probabilistic function ofan underlying Markov chain having state transitions that are notdirectly observable. The transitions between states are represented by atransition matrix A=[a(i,j)]. Each a(i,j) term of the transition matrixis the probability of making a transition to state j given that themodel is in state i. The output symbol probability of the model isrepresented by a set of functions B=[b(j)(O(t)], where the b(j)(O(t)term of the output symbol matrix is the probability of outputtingobservation O(t), given that the model is in state j. The first state isalways constrained to be the initial state for the first time frame ofthe utterance, as only a prescribed set of left-to-right statetransitions are possible. A predetermined final state is defined fromwhich transitions to other states cannot occur.

Transitions are restricted to reentry of a state or entry to one of thenext two states. Such transitions are defined in the model as transitionprobabilities. For example, a heart sound pattern currently having aframe of feature signals in state 2 has a probability of reenteringstate 2 of a(2,2), a probability a(2,3) of entering state 3 and aprobability of a(2,4)=1−a(2,1)−a(2,2) of entering state 4. Theprobability a(2, 1) of entering state 1 or the probability a(2,5) ofentering state 5 is zero and the sum of the probabilities a(2,1) througha(2,5) is one. Although the preferred embodiment restricts the flowgraphs to the present state or to the next two states, one skilled inthe art can build an HMM model without any transition restrictions.

The Markov model is formed for a reference pattern from a plurality ofsequences of training patterns and the output symbol probabilities aremultivariate Gaussian function probability densities. The heart soundtraverses through the feature extractor. During learning, the resultingfeature vector series is processed by a parameter estimator, whoseoutput is provided to the hidden Markov model. The hidden Markov modelis used to derive a set of reference pattern templates, each templaterepresentative of an identified S1-S4 pattern in a vocabulary set ofreference patterns. The Markov model reference templates are nextutilized to classify a sequence of observations into one of thereference patterns based on the probability of generating theobservations from each Markov model reference pattern template. Duringrecognition, the unknown pattern can then be identified as the referencepattern with the highest probability in the likelihood calculator.

In one embodiment, a heart sound analyzer detects Normal S1, Split S1,Normal S2, Normal split S2, Wide split S2, Paradoxical split S2, Fixedsplit S2, S3 right ventricle origin, S3 left ventricle origin, openingsnap, S4 right ventricle origin, S4 left ventricle origin, aorticejection sound, and pulmonic ejection sound, among others. The soundanalyzer can be an HMM type analyzer, a neural network type analyzer, afuzzy logic type analyzer, a genetic algorithm type analyzer, arule-based analyzer, or any suitable classifier. The heart sound data iscaptured, filtered, and the major features of the heart sound aredetermined and then operated by a classifier such as HMM or neuralnetwork, among others.

The analyzer can detect S1, whose major audible components are relatedto mitral and tricuspid valve closure. Mitral (MI) closure is the firstaudible component of the first sound. It normally occurs beforetricuspid (T1) closure, and is of slightly higher intensity than T1. Asplit of the first sound occurs when both components that make up thesound are separately distinguishable. In a normally split first sound,the mitral and tricuspid components are 20 to 30 milliseconds apart.Under certain conditions a wide or abnormally split first sound can beheard. An abnormally wide split first sound can be due to eitherelectrical or mechanical causes, which create asynchrony of the twoventricles. Some of the electrical causes may be right bundle branchblock, premature ventricular beats and ventricular tachycardia. Anapparently wide split can be caused by another sound around the time ofthe first. The closure of the aortic and pulmonic valves contributes tosecond sound production. In the normal sequence, the aortic valve closesbefore the pulmonic valve. The left sided mechanical events normallyprecede right sided events.

The system can analyze the second sound S2. The aortic (A2) component ofthe second sound is the loudest of the two components and is discernibleat all auscultation sites, but especially well at the base. The pulmonic(P2) component of the second sound is the softer of the two componentsand is usually audible at base left. A physiological split occurs whenboth components of the second sound are separately distinguishable.Normally this split sound is heard on inspiration and becomes single onexpiration. The A2 and P2 components of the physiological split usuallycoincide, or are less than 30 milliseconds apart during expiration andoften moved to around 50 to 60 milliseconds apart by the end ofinspiration. The physiological split is heard during inspiration becauseit is during that respiratory cycle that intrathoracic pressure drops.This drop permits more blood to return to the right heart. The increasedblood volume in the right ventricle results in a delayed pulmonic valveclosure. At the same time, the capacity of the pulmonary vessels in thelung is increased, which results in a slight decrease in the bloodvolume returning to the left heart. With less blood in the leftventricle, its ejection takes less time, resulting in earlier closing ofthe aortic valve. Therefore, the net effect of inspiration is to causeaortic closure to occur earlier, and pulmonary closure to occur later.Thus, a split second is heard during inspiration, and a single secondsound is heard during expiration. A reversed (paradoxical) split of thesecond sound occurs when there is a reversal of the normal closuresequence with pulmonic closure occurring before aortic. Duringinspiration the second sound is single, and during expiration the secondsound splits. This paradoxical splitting of the second sound may beheard when aortic closure is delayed, as in marked volume or pressureloads on the left ventricle (i.e., aortic stenosis) or with conductiondefects which delay left ventricular depolarization (i.e., left bundlebranch block). The normal physiological split second sound can beaccentuated by conditions that cause an abnormal delay in pulmonicvalve-1 closure. Such a delay may be due to an increased volume in theright ventricle as o compared with the left (atrial septal defect, orventricular septal defect); chronic right ventricular outflowobstruction (pulmonic stenosis); acute or chronic dilatation of the.right ventricle due to sudden rise in pulmonary artery pressure(pulmonary embolism); electrical delay or activation of AA the rightventricle (right bundle branch block); decreased elastic recoil of thepulmonary artery (idiopathic dilatation of the pulmonary artery). Thewide split has a duration of 40 to 50′ milliseconds, compared to thenormal physiologic split of 30 milliseconds. Fixed splitting of thesecond sound refers to split sound which displays little or norespiratory variation. The two components making up the sound occur intheir normal sequence, but the ventricles are unable to change theirvolumes with respiration. This finding is typical in atrial septaldefect, but is occasionally heard in congestive heart failure. The fixedsplit is heard best at base left with the diaphragm.

The third heart sound is also of low frequency, but it is heard justafter the second heart sound. It occurs in early diastole, during thetime of rapid ventricular filling. This sound occurs about 140 to 160milliseconds after the second sound. The S3 is often heard in normalchildren or young adults but when heard in individuals over the age of40 it usually reflects cardiac disease characterized by ventriculardilatation, decreased systolic function, and elevated ventriculardiastolic filling pressure. The nomenclature includes the termventricular gallop, protodiastolic gallop, S3 gallop, or the morecommon, S3. When normal it is referred to as a physiological third heartsound, and is usually not heard past the age of forty. The abnormal, orpathological third heart sound, may be heard in individuals withcoronary artery disease, cardiomyopathies, incompetent valves, left toright shunts, Ventricular Septal Defect (VSD), or Patent DuctusArteriosus (PDA). The pathological S3 may be the first clinical sign ofcongestive heart failure. The fourth heart sound is a low frequencysound heard just before the first heart sound, usually preceding thissound by a longer interval than that separating the two components ofthe normal first sound. It has also been known as an “atrial gallop”, a“presystolic gallop”, and an “S4 gallop”. It is most commonly known asan “S4”.

The S4 is a diastolic sound, which occurs during the late diastolicfilling phase at the time when the atria contract. When the ventricleshave a decreased compliance, or are receiving an increased diastolicvolume, they generate a low frequency vibration, the S4. Someauthorities believe the S4 may be normal in youth, but is seldomconsidered normal after the age of 20. The abnormal or pathological S4is heard in primary myocardial disease, coronary artery disease,hypertension, and aortic and pulmonic stenosis. The S4 may have itsorigin in either the left or right heart. The S4 of left ventricularorigin is best heard at the apex, with the patient supine, or in theleft lateral recumbent position. Its causes include severe hypertension,aortic stenosis, cardiomyopathies, and left ventricular myocardialinfarctions. In association with ischemic heart disease the S4 is oftenloudest during episodes of angina pectoris or may occur early after anacute myocardial infarction, often becoming fainter as the patientimproves. The S4 of right ventricular origin is best heard at the leftlateral sternal border. It is usually accentuated with inspiration, andmay be due to pulmonary stenosis, pulmonary hypertension, or rightventricular myocardial infarction. When both the third heart sound and afourth heart sound are present, with a normal heart rate, 60-100 heartbeats per minute, the four sound cadence of a quadruple rhythm may beheard.

Ejection sounds are high frequency clicky sounds occurring shortly afterthe first sound with the onset of ventricular ejection. They areproduced by the opening of the semilunar valves, aortic or pulmonic,either when one of these valves is diseased, or when ejection is rapidthrough a normal valve. They are heard best at the base, and may be ofeither aortic or pulmonic origin. Ejection sounds of aortic origin oftenradiate widely and may be heard anywhere on a straight line from thebase right to the apex. Aortic ejection sounds are most typically heardin patients with valvular aortic stenosis, but are occasionally heard invarious other conditions, such as aortic insufficiency, coarctation ofthe aorta, or aneurysm of the ascending aorta. Ejection sounds ofpulmonic origin are heard anywhere on a straight line from base left,where they are usually best heard, to the epigastrium Pulmonic ejectionsounds are typically heard in pulmonic stenosis, but may be encounteredin pulmonary hypertension, atrial septal defects (ASD) or in conditionscausing enlargement of the pulmonary artery. Clicks are high frequencysounds which occur in systole, either mid, early, or late. The clickgenerally occurs at least 100 milliseconds after the first sound. Themost common cause of the click is mitral valve prolapse. The clicks ofmitral origin are best heard at the apex, or toward the left lateralsternal border. The click will move closer to the first sound whenvolume to the ventricle is reduced, as occurs in standing or theValsalva maneuver. The opening snap is a short high frequency sound,which occurs after the second heart sound in early diastole. It usuallyfollows the second sound by about 60 to 100 milliseconds. It is mostfrequently the result of the sudden arrest of the opening of the mitralvalve, occurring in mitral stenosis, but may also be encountered inconditions producing increased flow through this valve (i.e., VSD orPDA). In tricuspid stenosis or in association with increased flow acrossthe tricuspid valve, as in ASD, a tricuspid opening snap may be heard.The tricuspid opening snap is loudest at the left lateral sternalborder, and becomes louder with inspiration.

Murmurs are sustained noises that are audible during the time periods ofsystole, diastole, or both. They are basically produced by thesefactors: 1) Backward regurgitation through a leaking valve or septaldefect; 2) Forward flow through a narrowed or deformed valve or conduitor through an arterial venous connection; 3) High rate of blood flowthrough a normal or abnormal valve; 4) Vibration of loose structureswithin the heart (i.e., chordae tendineae or valvular tissue). Murmursthat occur when the ventricles are contracting, that is, during systole,are referred to as systolic murmurs. Murmurs occurring when theventricles are relaxed and filling, that is during diastole, arereferred to as diastolic murmurs. There are six characteristics usefulin murmur identification and differentiation:

-   -   1) Location or the valve area over which the murmur is best        heard. This is one clue to the origin of the murmur. Murmurs of        mitral origin are usually best heard at the apex. Tricuspid        murmurs at the lower left lateral sternal border, and pulmonic        murmurs at base left. Aortic systolic murmurs are best heard at        base right, and aortic diastolic murmurs at Erb's point, the        third intercostal space to the left of the sternum.    -   2) Frequency (pitch). Low, medium, or high.    -   3) Intensity.    -   4) Quality.    -   5) Timing.(Occurring during systole, diastole, or both).    -   6) Areas where the sound is audible in addition to the area over        which it is heard best.

Systolic murmurs are sustained noises that are audible during the timeperiod of systole, or the period between S1 and S2. Forward flow acrossthe aortic or pulmonic valves, or regurgitant flow from the mitral ortricuspid valve may produce a systolic murmur. Systolic murmurs may benormal, and can represent normal blood flow, i.e., thin chest, babiesand children, or increased blood flow, i.e., pregnant women. Earlysystolic murmurs begin with or shortly after the first sound and peak inthe first third of systole. Early murmurs have the greatest intensity inthe early part of the cycle. The commonest cause is the innocent murmurof childhood (to be discussed later). A small ventricular septal defect(VSD) occasionally causes an early systolic murmur. The early systolicmurmur of a small VSD begins with S1 and stops in mid systole, becauseas ejection continues and the ventricular size decreases, the smalldefect is sealed shut, causing the murmur to soften or cease. Thismurmur is characteristic of the type of children's VSD located in themuscular portion of the ventricular septum. This defect may disappearwith age. A mid-systolic murmur begins shortly after the first sound,peaks in the middle of systole, and does not quite extend to the secondsound. It is the crescendo decrescendo murmur which builds up anddecrease symmetrically. It is also known as an ejection murmur. It mostcommonly is due to forward blood flow through a normal, narrow orirregular valve, i.e., aortic or pulmonic stenosis. The murmur beginswhen the pressure in the respective ventricle exceeds the aortic orpulmonary arterial pressure. The most characteristic feature of thismurmur is its cessation before the second sound, thus leaving thislatter sound identifiable as a discrete entity. This type of murmur iscommonly heard in normal individuals, particularly in the young, whousually have increased blood volumes flowing over normal valves. In thissetting the murmur is usually short, with its peak intensity early insystole, and is soft, seldom over 2 over 6 in intensity. It is thendesignated as an innocent murmur. In order for a murmur to be classifiedas innocent (i.e. normal), the following are present:

-   -   1) Normal splitting of the second sound together with absence of        abnormal sounds or murmurs, such as ejection sounds, diastolic        murmurs, etc.    -   2) Normal jugular venus and carotid pulses    -   3) Normal precordial pulsations or palpation, and    -   4) Normal chest x-ray and ECG

Obstruction or stenosis across the aortic or pulmonic valves also maygive rise to a murmur of this type. These murmurs are usually longer andlouder than the innocent murmur, and reach a peak intensity inmid-systole. The murmur of aortic stenosis is harsh in quality and isheard equally well with either the bell or the diaphragm. It is heardbest at base right, and radiates to the apex and to the neckbilaterally.

An early diastolic murmur begins with a second sound, and peaks in thefirst third of diastole. Common causes are aortic regurgitation andpulmonic regurgitation. The early diastolic murmur of aorticregurgitation usually has a high frequency blowing quality, is heardbest with a diaphragm at Erb's point, and radiates downward along theleft sternal border. Aortic regurgitation tends to be of short duration,and heard best on inspiration. This respiratory variation is helpful indifferentiating pulmonic regurgitation from aortic regurgitation. Amid-diastolic murmur begins after the second sound and peaks inmid-diastole. Common causes are mitral stenosis, and tricuspid stenosis.The murmur of mitral stenosis is a low frequency, crescendo de crescendorumble, heard at the apex with the bell lightly held. If it radiates, itdoes so minimally to the axilla. Mitral stenosis normally produces threedistinct abnormalities which can be heard: 1) A loud first sound 2) Anopening snap, and 3) A mid-diastolic rumble with a late diastolicaccentuation.A late diastolic murmur occurs in the latter half of diastole,synchronous with atrial contraction, and extends to the first sound.Although occasionally occurring alone, it is usually a component of thelonger diastolic murmur of mitral stenosis or tricuspid stenosis. Thismurmur is low in frequency, and rumbling in quality. A continuous murmurusually begins during systole and extends through the second sound andthroughout the diastolic period. It is usually produced as a result ofone of four mechanisms: 1) An abnormal communication between an arteryand vein; 2) An abnormal communication between the aorta and the rightside of the heart or with the left atrium; 3) An abnormal increase inflow, or constriction in an artery; and 4) Increased or turbulent bloodflow through veins. Patent Ductus Arteriosus (PDA) is the classicalexample of this murmur. This condition is usually corrected inchildhood. It is heard best at base left, and is usually easily audiblewith the bell or diaphragm. Another example of a continuous murmur isthe so-called venous hum, but in this instance one hears a constantroaring sound which changes little with the cardiac cycle. A latesystolic murmur begins in the latter half of systole, peaks in the laterthird of systole, and extends to the second sound. It is a modifiedregurgitant murmur with a backward flow through an incompetent valve,usually the mitral valve. It is commonly heard in mitral valve prolapse,and is usually high in frequency (blowing in quality), and heard bestwith a diaphragm at the apex. It may radiate to the axilla or leftsternal border. A pansystolic or holosystolic murmur is heardcontinuously throughout systole. It begins with the first heart sound,and ends with the second heart sound. It is commonly heard in mitralregurgitation, tricuspid regurgitation, and ventricular septal defect.This type of murmur is caused by backward blood flow. Since the pressureremains higher throughout systole in the ejecting chamber than in thereceiving chamber, the murmur is continuous throughout systole.Diastolic murmurs are sustained noises that are audible between S2 andthe next S. Unlike systolic murmurs, diastolic murmurs should usually beconsidered pathological, and not normal. Typical abnormalities causingdiastolic murmurs are aortic regurgitation, pulmonic regurgitation,mitral stenosis, and tricuspid stenosis. The timing of diastolic murmursis the primary concern of this program. These murmurs can be early, mid,late and pan in nature. In a pericardial friction rub, there are threesounds, one systolic, and two diastolic. The systolic sound may occuranywhere in systole, and the two diastolic sounds occur at the times theventricles are stretched. This stretching occurs in early diastole, andat the end of diastole. The pericardial friction rub has a scratching,grating, or squeaking leathery quality. It tends to be high in frequencyand best heard with a diaphragm. A pericardial friction rub is a sign ofpericardial inflammation and may be heard in infective pericarditis, inmyocardial infarction, following cardiac surgery, trauma, and inautoimmune problems such as rheumatic fever.

In addition to heart sound analysis, the timing between the onset andoffset of particular features of the ECG (referred to as an interval)provides a measure of the state of the heart and can indicate thepresence of certain cardiological conditions. An EKG analyzer isprovided to interpret EKG/ECG data and generate warnings if needed. Theanalyzer examines intervals in the ECG waveform such as the QT intervaland the PR interval. The QT interval is defined as the time from thestart of the QRS complex to the end of the T wave and corresponds to thetotal duration of electrical activity (both depolarization andrepolarization) in the ventricles. Similarly, the PR interval is definedas the time from the start of the P wave to the start of the QRS complexand corresponds to the time from the onset of atrial depolarization tothe onset of ventricular depolarization. In one embodiment, hiddenMarkov and hidden semi-Markov models are used for automaticallysegmenting an electrocardiogram waveform into its constituent waveformfeatures. An undecimated wavelet transform is used to generate anovercomplete representation of the signal that is more appropriate forsubsequent modelling. By examining the ECG signal in detail it ispossible to derive a number of informative measurements from thecharacteristic ECG waveform. These can then be used to assess themedical well-being of the patient. The wavelet methods such as theundecimated wavelet transform, can be used instead of raw time seriesdata to generate an encoding of the ECG which is tuned to the uniquespectral characteristics of the ECG waveform features. The segmentationprocess can use of explicit state duration modelling with hiddensemi-Markov models. Using a labelled data set of ECG waveforms, a hiddenMarkov model is trained in a supervised manner. The model was comprisedof the following states: P wave, QRS complex, T wave, U wave, andBaseline. The parameters of the transition matrix aij were computedusing the maximum likelihood estimates. The ECG data is encoded withwavelets from the Daubechies, Symlet, Coiflet or Biorthogonal waveletfamilies, among others. In the frequency domain, a wavelet at a givenscale is associated with a bandpass filter of a particular centrefrequency. Thus the optimal wavelet basis will correspond to the set ofbandpass filters that are tuned to the unique spectral characteristicsof the ECG. In another implementation, a hidden semi-Markov model (HSMM)is used. HSMM differs from a standard HMM in that each of theself-transition coefficients aii are set to zero, and an explicitprobability density is specified for the duration of each state. In thisway, the individual state duration densities govern the amount of timethe model spends in a given state, and the transition matrix governs theprobability of the next state once this time has elapsed. Thus theunderlying stochastic process is now a “semi-Markov” process. To modelthe durations of the various waveform features of the ECG, a Gammadensity is used since this is a positive distribution which is able tocapture the skewness of the ECG state durations. For each state i,maximum likelihood estimates of the shape and scale parameters werecomputed directly from the set of labelled ECG signals.

In addition to providing beat-to-beat timing information for othersensors to use, the patterns of the constituent waveform featuresdetermined by the HMM or neural networks, among other classifiers, canbe used for detecting heart attacks or stroke attacks, among others. Forexample, the detection and classification of ventricular complexes fromthe ECG data is can be used for rhythm and various types of arrhythmiato be recognized. The system analyzes pattern recognition parameters forclassification of normal QRS complexes and premature ventricularcontractions (PVC). Exemplary parameters include the width of the QRScomplex, vectorcardiogram parameters, amplitudes of positive andnegative peaks, area of positive and negative waves, varioustime-interval durations, amplitude and angle of the QRS vector, amongothers. The EKG analyzer can analyze EKG/ECG patterns for Hypertrophy,Enlargement of the Heart, Atrial Enlargement, Ventricular Hypertrophy,Arrhythmias, Ectopic Supraventricular Arrhythmias, VentricularTachycardia (VT), Paroxysmal Supraventricular Tachycardia (PSVT),Conduction Blocks, AV Block, Bundle Branch Block, Hemiblocks,Bifascicular Block, Preexcitation Syndromes, Wolff-Parkinson-WhiteSyndrome, Lown-Ganong-Levine Syndrome, Myocardial Ischemia, Infarction,Non-Q Wave Myocardial Infarction, Angina, Electrolyte Disturbances,Heart Attack, Stroke Attack, Hypothermia, Pulmonary Disorder, CentralNervous System Disease, or Athlete's Heart, for example.

FIG. 16C shows an exemplary process to detect stroke attack. In thisembodiment, 3D accelerometer sensing is used. First, the process looksfor weakness (hemiparesis) in either the left half or the right half ofthe body, for example the left/right arms, legs, or face (3000). Next,the system analyzes walking pattern to see if the patient has a loss ofbalance or coordination (3002). The system then asks the user to movehands/feet in a predetermined pattern (3004) and reads accelerometeroutput in accordance with predetermined pattern movement (3006). Forexample, the system can ask the user to point his/her right or left handto the nose. The accelerometer outputs are tested to check if thecorrect hand did reach the nose. In another example, the user can beprompted to extend his or her hands on both side and wiggle the hands orto kick the legs. Again, the outputs of the accelerometers are used toconfirm that the user is able to follow direction. The accelerometeroutputs are provided to a pattern classifier, which can be an HMM, aneural network, a Bayesian network, fuzzy logic, or any suitableclassifiers (3008). The system also checks whether patient isexperiencing dizziness or sudden, severe headache with no known cause(3010). Next, the system displays a text image and asks the patient toread back the text image, one eye at a time (3012). Using a speechrecognizer module, the user speech is converted into text to compareagainst the text image. The speech recognizer also detects if the userexhibits signs of confusion, trouble speaking or understanding (3014).The system also asks the patient if they feel numbness in the body-arms,legs, face (3016). Next the system asks the patient to squeezegauge/force sensor to determine force applied during squeeze (3018). Ifany of the above tests indicate a possible stroke, the system displays awarning to the patient and also connects the patient to the appropriateemergency response authority, family member, or physician.

In one implementation, an HMM is used to track patient motor skills orpatient movement patterns. Human movement involves a periodic motion ofthe legs. Regular walking involves the coordination of motion at thehip, knee and ankle, which consist of complex joints. The musculargroups attached at various locations along the skeletal structure oftenhave multiple functions. The majority of energy expended during walkingis for vertical motion of the body. When a body is in contact with theground, the downward force due to gravity is reflected back to the bodyas a reaction to the force. When a person stands still, this groundreaction force is equal to the person's weight multiplied bygravitational acceleration. Forces can act in other directions. Forexample, when we walk, we also produce friction forces on the ground.When the foot hits the ground at a heel strike, the friction between theheel and the ground causes a friction force in the horizontal plane toact backwards against the foot. This force therefore causes a breakingaction on the body and slows it down. Not only do people accelerate andbrake while walking, they also climb and dive. Since reaction force ismass times acceleration, any such acceleration of the body will bereflected in a reaction when at least one foot is on the ground. Anupwards acceleration will be reflected in an increase in the verticalload recorded, while a downwards acceleration will be reduce theeffective body weight. Zigbee wireless sensors with tri-axialaccelerometers are mounted to the patient on different body locationsfor recording, for example the tree structure as shown in FIG. 16D. Asshown therein, sensors can be placed on the four branches of the linksconnect to the root node (torso) with the connected joint, left shoulder(LS), right shoulder (RS), left hip (LH), and right hip (RH).Furthermore, the left elbow (LE), right elbow (RE), left knee (LK), andright knee (RK) connect the upper and the lower extremities. Thewireless monitoring devices can also be placed on upper back body nearthe neck, mid back near the waist, and at the front of the right legnear the ankle, among others.

The sequence of human motions can be classified into several groups ofsimilar postures and represented by mathematical models calledmodel-states. A model-state contains the extracted features of bodysignatures and other associated characteristics of body signatures.Moreover, a posture graph is used to depict the inter-relationshipsamong all the model-states, defined as PG(ND,LK), where ND is a finiteset of nodes and LK is a set of directional connections between everytwo nodes. The directional connection links are called posture links.Each node represents one model-state, and each link indicates atransition between two model-states. In the posture graph, each node mayhave posture links pointing to itself or the other nodes.

In the pre-processing phase, the system obtains the human body profileand the body signatures to produce feature vectors. In the modelconstruction phase, the system generate a posture graph, examinefeatures from body signatures to construct the model parameters of HMM,and analyze human body contours to generate the model parameters ofASMs. In the motion analysis phase, the system uses features extractedfrom the body signature sequence and then applies the pre-trained HMM tofind the posture transition path, which can be used to recognize themotion type. Then, a motion characteristic curve generation procedurecomputes the motion parameters and produces the motion characteristiccurves. These motion parameters and curves are stored over time, and ifdifferences for the motion parameters and curves over time is detected,the system then runs the patient through additional tests to confirm astroke attack, and if a stroke attack is suspected, the system promptsthe user to seek medical attention immediately and preferably within the3 hour for receiving TPA.

FIG. 16E shows one exemplary process for determining weakness in theleft or right half of the body. The process compares historical leftshoulder (LS) strength against current LS strength (3200). The processalso compares historical right shoulder (RS) strength against current RSstrength (3202). The process can compare historical left hip (LH)strength against current LH strength (3204). The process can alsocompare historical right hip (RH) strength against current RH strength(3206). If the variance between historical and current strength exceedsthreshold, the process generates warnings (3208). Furthermore, similarcomparisons can be made for sensors attached to the left elbow (LE),right elbow (RE), left knee (LK), and right knee (RK) connect the upperand the lower extremities, among others.

The system can ask the patient to squeeze a strength gauge,piezoelectric sensor, or force sensor to determine force applied duringsqueeze. The user holds the sensor or otherwise engages the sensor. Theuser then applies and holds a force (e.g., compression, torque, etc.) tothe sensor, which starts a timer clock and triggers a sampling startindicator to notify the user to continue to apply (maximum) force to thesensor. Strength measurements are then sampled periodically during thesampling period until the expiration of time. From the sampled strengthdata, certain strength measurement values are selected, such as themaximum value, average value(s), or values obtained during the samplingperiod. The user can test both hands at the same time, or alternativelyhe may test one hand at a time. A similar approach is used to sense legstrength, except that the user is asked to pushed down on a scale todetermine the foot force generated by the user.

The system can detect hemiparesis, a very common symptom of stroke, bydetecting muscular weakness or partial paralysis to one side of thebody. Additionally, the accelerometers can detect ataxia, which is animpaired ability to perform smooth coordinated voluntary movements.Additionally, the system can detect aphasia, including receptive aphasiaand expressive aphasia. Aphasia is a cognitive disorder marked by animpaired ability to comprehend (receptive aphasia) or express(expressive aphasia) language. Exemplary embodiments are disclosed fordetecting receptive aphasia by displaying text or playing verbalinstructions to the user, followed by measuring the correctness and/ortime delay of the response from the user. Exemplary embodiments are alsodisclosed for detecting expressive aphasia by positing sound made by ananimal to the user, prompting the user to identify or name the animal,and measuring the correctness and/or time delay of the response from theuser. The system can also detect dysarthria, a disorder of speecharticulation (e.g., slurred speech), by prompting the user to say a wordor phrase that is recorded for subsequent comparison by voice patternrecognition or evaluation by medical personnel.

In the above manner, the system automatically reminds the user to gethelp if he feels a sudden numbness or weakness of the face, arm or leg,especially on one side of the body, sudden confusion, trouble speakingor understanding, sudden trouble seeing in one or both eyes, or suddentrouble walking, dizziness, loss of balance or coordination.

In one embodiment, the accelerometers distinguish between lying down andeach upright position of sitting and standing based on the continuousoutput of the 3D accelerometer. The system can detect (a) extended timein a single position; (b) extended time sitting in a slouching posture(kyphosis) as opposed to sitting in an erect posture (lordosis); and (c)repetitive stressful movements, such as may be found on somemanufacturing lines, while typing for an extended period of time withoutproper wrist support, or while working all day at a job lifting boxes,among others. In one alternative embodiment, angular position sensors,one on each side of the hip joint, can be used to distinguish lyingdown, sitting, and standing positions. In another embodiment, thepresent invention repeatedly records position and/or posture data overtime. In one embodiment, magnetometers can be attached to a thigh andthe torso to provide absolute rotational position about an axiscoincident with Earth's gravity vector (compass heading, or yaw). Inanother embodiment, the rotational position can be determined throughthe in-door positioning system as discussed above.

Depending on the severity of the stroke, patients can experience a lossof consciousness, cognitive deficits, speech dysfunction, limb weakness,hemiplegia, vertigo, diplopia, lower cranial nerve dysfunction, gazedeviation, ataxia, hemianopia, and aphasia, among others. Four classicsyndromes that are characteristically caused by lacunar-type stroke are:pure motor hemiparesis, pure sensory syndrome, ataxic hemiparesissyndrome, and clumsy-hand dysarthria syndrome. Patients with pure motorhemiparesis present with face, arm, and leg weakness. This conditionusually affects the extremities equally, but in some cases it affectsone extremity more than the other. The most common stroke location inaffected patients is the posterior limb of the internal capsule, whichcarries the descending corticospinal and corticobulbar fibers. Otherstroke locations include the pons, midbrain, and medulla. Pure sensorysyndrome is characterized by hemibody sensory symptoms that involve theface, arm, leg, and trunk. It is usually the result of an infarct in thethalamus. Ataxic hemiparesis syndrome features a combination ofcerebellar and motor symptoms on the same side of the body. The leg istypically more affected than the arm. This syndrome can occur as aresult of a stroke in the pons, the internal capsule, or the midbrain,or in the anterior cerebral artery distribution. Patients withclumsy-hand dysarthria syndrome experience unilateral hand weakness anddysarthria. The dysarthria is often severe, whereas the hand involvementis more subtle, and patients may describe their hand movements as“awkward.” This syndrome is usually caused by an infarct in the pons.

Different patterns of signs can provide clues as to both the locationand the mechanism of a particular stroke. The system can detect symptomssuggestive of a brainstem stroke include vertigo, diplopia, bilateralabnormalities, lower cranial nerve dysfunction, gaze deviation (towardthe side of weakness), and ataxia. Indications of higher corticaldysfunction—such as neglect, hemianopsia, aphasia, and gaze preference(opposite the side of weakness)-suggest hemispheric dysfunction withinvolvement of a superficial territory from an atherothrombotic orembolic occlusion of a mainstem vessel or peripheral branch.

The system can detect a pattern of motor weakness. Ischemia of thecortex supplied by the middle cerebral artery typically causes weaknessthat (1) is more prominent in the arm than in the leg and (2) involvesthe distal muscles more than the proximal muscles. Conversely,involvement of an area supplied by the superficial anterior cerebralartery results in weakness that (1) is more prominent in the leg thanthe arm and (2) involves proximal upper extremity (shoulder) musclesmore than distal upper extremity muscles. Flaccid paralysis of both thearm and leg (unilateral) suggests ischemia of the descending motortracts in the basal ganglia or brainstem. This is often caused by anocclusion of a penetrating artery as a result of small-vessel disease.Once the stroke is detected, intravenous (IV) tissue plasminogenactivator (t-PA) needs to be given within 3 hours of symptom onset. Anaccurate assessment of the timing of the stroke is also crucial. Thesystem keeps track of the timing off the onset of the stroke for thispurpose.

One major symptom of a stroke is unexplained weakness or numbness in themuscle. To detect muscle weakness or numbness, in one embodiment, thesystem applies a pattern recognizer such as a neural network or a HiddenMarkov Model (HMM) to analyze accelerometer output. In anotherembodiment, electromyography (EMG) is used to detect muscle weakness. Inanother embodiment, EMG and a pattern analyzer is used to detect muscleweakness. In yet another embodiment, a pattern analyzer analyzes bothaccelerometer and EMG data to determine muscle weakness. In a furtherembodiment, historical ambulatory information (time and place) is usedto further detect changes in muscle strength. In yet other embodiments,accelerometer data is used to confirm that the patient is at rest sothat EMG data can be accurately captured or to compensate for motionartifacts in the EMG data in accordance with a linear or non-linearcompensation table. In yet another embodiment, the EMG data is used todetect muscle fatigue and to generate a warning to the patient to get toa resting place or a notification to a nurse or caregiver to rendertimely assistance.

The amplitude of the EMG signal is stochastic (random) in nature and canbe reasonably represented by a Gausian distribution function. Theamplitude of the signal can range from 0 to 10 mV (peak-to-peak) or 0 to1.5 mV (rms). The usable energy of the signal is limited to the 0 to 500Hz frequency range, with the dominant energy being in the 50-150 Hzrange. Usable signals are those with energy above the electrical noiselevel. The dominant concern for the ambient noise arises from the 60 Hz(or 50 Hz) radiation from power sources. The ambient noise signal mayhave an amplitude that is one to three orders of magnitude greater thanthe EMG signal. There are two main sources of motion artifact: one fromthe interface between the detection surface of the electrode and theskin, the other from movement of the cable connecting the electrode tothe amplifier. The electrical signals of both noise sources have most oftheir energy in the frequency range from 0 to 20 Hz and can be reduced.

As shown in FIG. 17A, to eliminate the potentially much greater noisesignal from power line sources, a differential instrumentation amplifieris employed. Any signal that originates far away from the detectionsites will appear as a common signal, whereas signals in the immediatevicinity of the detection surfaces will be different and consequentlywill be amplified. Thus, relatively distant power lines noise signalswill be removed and relatively local EMG signals will be amplified. Thesource impedance at the junction of the skin and detection surface mayrange from several thousand ohms to several megohms for dry skin. Inorder to prevent attenuation and distortion of the detected signal dueto the effects of input loading, the input impedance of the differentialamplifier is as large as possible, without causing ancillarycomplications to the workings of the differential amplifier. The signalto noise ratio is increased by filtering between 20-500 Hz with aroll-off of 12 dB/octave.

In one embodiment, direct EMG pre-amplification at the skin surfaceprovides the best myoelectric signal quality for accurate, reliable EMGsignal detection and eliminates cable motion artifact. Thedouble-differential instrumentation pre-amplifier design attenuatesunwanted common-mode bioelectric signals to reduce cross-talk fromadjacent muscle groups. Internal RFI and ESD protection prevents radiofrequency interference and static damage. The constant low-impedanceoutput of the pre-amplifier completely eliminates cable noise and cablemotion artifacts without requiring any additional signal processingwithin the pre-amplifier. An integral ground reference plane providesimmunity to electromagnetic environmental noise. All signal and powerconductors in the pre-amplifier cable are enclosed inside anindependent, isolated shield to eliminate interference from ACpower-lines and other sources. The contacts are corrosion-free, medicalgrade stainless steel for maximal signal flow. The system usesbiocompatible housing and sensor materials to prevent allergicreactions.

In one implementation, MA-311 EMG pre-amplifiers from Motion LabSystems, Inc., Baton Rouge, La., can be used. The pre-amplifiersincorporate both radio frequency interference (RFI) filters andelectrostatic discharge (ESD) protection circuitry resulting in anextraordinarily reliable EMG pre-amplifier that can be used in almostany environment. Featuring a double-differential input, the uniquedesign of the Motion Lab Systems EMG pre-amplifiers enables researchersto produce high-quality, low-noise EMG data from subjects under the mostadverse conditions (e.g. on treadmills, using mobile phones etc.)without any skin preparation or subsequent signal processing.

In another implementation, a micro-powered EMG embodiment includes aninstrumentation amplifier and an AC coupling that maintains a high CMRRwith a gain of about 1000. The electronic circuits are mounted on aflexible circuit board (FPC) with slidable electrode settings thatallows differential recording at various distances between theelectrodes. The high gain amplifier is placed next to the recordingelectrodes to achieve high SNR. Battery power provides isolation and lownoise at various frequencies that would likely not be fully attenuatedby the PSRR and causing alias errors.

The system can detect dominant symptoms of stroke can include weaknessor paralysis of the arms and/or legs, incoordination (ataxia), numbnessin the arms/legs using accelerometers or EMG sensors. The EMG sensorscan detect muscle fatigue and can warn the patient to get to a restingarea if necessary to prevent a fall. The system can detect partial/totalloss of vision by asking the patient to read a predetermined phrase anddetect slur using speech recognizer. The system can detect loss ofconsciousness/coma by detecting lack of movement. Voice/speechdisturbances are not initially the dominant symptoms in stroke, and thedisturbances can be detected by a speech recognizer. In oneimplementation, the system uses PNL (probabilistic networks library) todetect unusual patient movement/ambulatory activities that will lead toa more extensive check for stroke occurrence. PNL supports dynamic Bayesnets, and factor graphs; influence diagrams. For inference, PNL supportsexact inference using the junction tree algorithm, and approximateinference using loopy belief propagation or Gibbs sampling. Learning canbe divided along many axes: parameter or structure, directed orundirected, fully observed or partially observed, batch or online,discriminative or maximum likelihood, among others. First, the systemperforms data normalization and filtering for the accelerometers and EMGsensors that detect patient movements and muscle strength. The data caninclude in-door positioning information, 3D acceleration information, orEMG/EKG/EEG data, for example. The data can be processed using waveletas discussed above or using any suitable normalization/filteringtechniques. Next, the system performs parameterization anddiscretization. The Bayesian network is adapted in accordance with apredefined network topology. The system also defines conditionalprobability distributions. The system then generates the probability ofevent P(y), under various scenarios. Training data is acquired and atraining method is built for the Bayesian network engine. Next, thesystem tunes model parameters and performs testing on the thus formedBayesian network.

In one embodiment, a housing (such as a strap, a wrist-band, or a patch)provides a plurality of sensor contacts for EKG and/or EMG. The samecontacts can be used for detecting EKG or EMG and can be placed as twoparallel contacts (linear or spot shape) on opposite sides of the band,two adjacent parallel contacts on the inner surface of the band, twoparallel adjacent contacts on the back of the wrist-watch, oralternatively one contact on the back of the watch and one contact onthe wrist-band. The outputs of the differential contacts are filtered toremove motion artifacts. The differential signal is captured, andsuitably filtered using high pass/low pass filters to remove noise, anddigitized for signal processing. In one embodiment, separate amplifiersare used to detect EKG (between 50 mHz and 200 Hz) and for EMG (between10 Hz and 500 Hz). In another embodiment, one common amp is used forboth EKG/EMG, and software filter is applied to the digitized signal toextract EKG and EMG signals, respectively. The unit can apply Waveletprocessing to convert the signal into the frequency domain and applyrecognizers such as Bayesian, NN or HMM to pull the EMG or EKG signalsfrom noise. The system uses a plurality of wireless nodes to transmitposition and to triangulate with the mobile node to determine position.3D accelerometer outputs can be integrated to provide movement vectorsand positioning information. Both radio triangulation and accelerometerdata can confirm the position of the patient. The RF signature of aplurality of nodes with known position can be used to detect proximityto a particular node with a known position and the patient's positioncan be extrapolated therefrom.

In one embodiment, Analog Device's AD627, a micro-power instrumentationamplifier, is used for differential recordings while consuming lowpower. In dual supply mode, the power rails Vs can be as low as ±1.1Volt, which is ideal for battery-powered applications. With a maximumquiescent current of 85 μA (60 μA typical), the unit can operatecontinuously for several hundred hours before requiring batteryreplacement. The batteries are lithium cells providing 3.0V to becapable of recording signals up to +1 mV to provide sufficient margin todeal with various artifacts such as offsets and temperature drifts. Theamplifier's reference is connected to the analog ground to avoidadditional power consumption and provide a low impedance connection tomaintain the high CMRR. To generate virtual ground while providing lowimpedance at the amplifier's reference, an additional amplifier can beused. In one implementation, the high-pass filtering does not requireadditional components since it is achieved by the limits of the gainversus frequency characteristics of the instrumentation amplifier. Theamplifier has been selected such that with a gain of 60 dB, a flatresponse could be observed up to a maximum of 100 Hz with gainattenuation above 100 Hz in one implementation.

In another implementation, a high pass filter is used so that thecut-off frequency becomes dependent upon the gain value of the unit. Thebootstrap AC-coupling maintains a much higher CMRR so critical indifferential measurements. Assuming that the skin-electrode impedancemay vary between 5 K- and 10 K-ohms, 1 M-ohm input impedance is used tomaintain loading errors below acceptable thresholds between 0.5% and 1%.

When an electrode is placed on the skin, the detection surfaces come incontact with the electrolytes in the skin. A chemical reaction takesplace which requires some time to stabilize, typically in the order of afew seconds. The chemical reaction should remain stable during therecording session and should not change significantly if the electricalcharacteristics of the skin change from sweating or humidity changes.The active electrodes do not require any abrasive skin preparation andremoval of hair. The electrode geometry can be circular or can beelongated such as bars. The bar configuration intersects more fibers.The inter detection-surface distance affects the bandwidth and amplitudeof the EMG signal; a smaller distance shifts the bandwidth to higherfrequencies and lowers the amplitude of the signal. An interdetection-surface of 1.0 cm provides one configuration that detectsrepresentative electrical activity of the muscle during a contraction.The electrode can be placed between a motor point and the tendoninsertion or between two motor points, and along the longitudinalmidline of the muscle. The longitudinal axis of the electrode (whichpasses through both detection surfaces) should be aligned parallel tothe length of the muscle fibers. The electrode location is positionedbetween the motor point (or innervation zone) and the tendinousinsertion, with the detection surfaces arranged so that they intersectas many muscle fibers as possible.

In one embodiment, a multi-functional bio-data acquisition providesprogrammable multiplexing of the same differential amplifiers forextracting EEG (electroencephalogram), ECG (electrocardiogram), or EMG(electromyogram) waves. The system includes an AC-coupled choppedinstrumentation amplifier, a spike filtering stage, a constant gainstage, and a continuous-time variable gain stage, whose gain is definedby the ratio of the capacitors. The system consumes microamps from 3V.The gain of the channel can be digitally set to 400, 800, 1600 or 2600.Additionally, the bandwidth of the circuit can be adjusted via thebandwidth select switches for different biopotentials. The high cut-offfrequency of the circuit can be digitally selected for differentapplications of EEG acquisition.

In another embodiment, a high-resolution, rectangular, surface arrayelectrode-amplifier and associated signal conditioning circuitrycaptures electromyogram (EMG) signals. The embodiment has a rectangulararray electrode-amplifier followed by a signal conditioning circuit. Thesignal conditioning circuit is generic, i.e., capable of receivinginputs from a number of different/interchangeable EMG/EKG/EEGelectrode-amplifier sources (including from both monopolar and bipolarelectrode configurations). The electrode-amplifier is cascaded with aseparate signal conditioner minimizes noise and motion artifact bybuffering the EMG signal near the source (the amplifier presents a veryhigh impedance input to the EMG source, and a very low outputimpedance); minimizes noise by amplifying the EMG signal early in theprocessing chain (assuming the electrode-amplifier includes signal gain)and minimizes the physical size of this embodiment by only including afirst amplification stage near the body. The signals are digitized andtransmitted over a wireless network such as WiFI, Zigbee, or Bluetoothtransceivers and processed by the base station that is remote from thepatient. For either high-resolution monopolar arrays or classicalbipolar surface electrode-amplifiers, the output of theelectrode-amplifier is a single-ended signal (referenced to the isolatedreference). The electrode-amplifier transduces and buffers the EMGsignal, providing high gain without causing saturation due to eitheroffset potentials or motion artifact. The signal conditioning circuitprovides selectable gain (to magnify the signal up to the range of thedata recording/monitoring instrumentation, high-pass filtering (toattenuate motion artifact and any offset potentials), electricalisolation (to prevent injurious current from entering the subject) andlow-pass filtering (for anti-aliasing and to attenuate noise out of thephysiologic frequency range).

FIGS. 17B and 17C shows exemplary sEMG outputs from a differentialamplifier to detect muscle strength. FIG. 17B shows the left and rightbody EMG signals for the patient in a normal state, while FIG. 17Cillustrates a patient with degraded muscle capability. In general,muscle fire in a smooth fashion in normal individuals, with littleirritability or fasiculation. Muscles fire symmetrically in healthystate when comparing left and right sides in motion. In general, healthypatients show a greater consistency in muscle patterns than injuredpatients, with the injured showing a greater increase in variability.Such variations are detected by EMG analyzers.

The EMG signal can be rectified, integrated a specified interval of andsubsequently forming a time series of the integrated values. The systemcan calculate the root-mean-squared (rms) and the average rectified(avr) value of the EMG signal. The system can also determine musclefatigue through the analysis of the frequency spectrum of the signal.The system can also assess neurological diseases which affect the fibertyping or the fiber cross-sectional area of the muscle. Variousmathematical techniques and Artificial Intelligence (AI) analyzer can beapplied. Mathematical models include wavelet transform, time-frequencyapproaches, Fourier transform, Wigner-Ville Distribution (WVD),statistical measures, and higher-order statistics. AI approaches towardssignal recognition include Artificial Neural Networks (ANN), dynamicrecurrent neural networks (DRNN), fuzzy logic system, Genetic Algorithm(GA), and Hidden Markov Model (HMM).

A single-threshold method or alternatively a double threshold method canbe used which compares the EMG signal with one or more fixed thresholds.The embodiment is based on the comparison of the rectified raw signalsand one or more amplitude thresholds whose value depends on the meanpower of the background noise. Alternatively, the system can performspectrum matching instead of waveform matching techniques when theinterference is induced by low frequency baseline drift or by highfrequency noise.

EMG signals are the superposition of activities of multiple motor units.The EMG signal can be decomposed to reveal the mechanisms pertaining tomuscle and nerve control. Decomposition of EMG signal can be done bywavelet spectrum matching and principle component analysis of waveletcoefficients where the signal is de-noised and then EMG spikes aredetected, classified and separated. In another embodiment, principlecomponents analysis (PAC) for wavelet coefficients is used with thefollowing stages: segmentation, wavelet transform, PCA, and clustering.EMG signal decomposition can also be done using non-linear least meansquare (LMS) optimization of higher-order cumulants.

Time and frequency domain approaches can be used. The wavelet transform(WT) is an efficient mathematical tool for local analysis ofnon-stationary and fast transient signals. One of the main properties ofWT is that it can be implemented by means of a discrete time filterbank. The Fourier transforms of the wavelets are referred as WT filters.The WT represents a very suitable method for the classification of EMGsignals. The system can also apply Cohen class transformation,Wigner-Ville distribution (WVD), and Choi-Williams distribution or othertime-frequency approaches for EMG signal processing.

In Cohen class transformation, the class time-frequency representationis particularly suitable to analyze surface myoelectric signals recordedduring dynamic contractions, which can be modeled as realizations ofnonstationary stochastic process. The WVD is a time-frequency that candisplay the frequency as a function of time, thus utilizing allavailable information contained in the EMG signal. Although the EMGsignal can often be considered as quasi-stationary there is stillimportant information that is transited and may be distinguished by WVD.

Implementing the WVD with digital computer requires a discrete form.This allows the use of fast Fourier transform (FFT), which produces adiscrete-time, discrete-frequency representation. The common type oftime frequency distribution is the Short-time Fourier Transform (STFT).The Choi-Williams method is a reduced interference distribution. TheSTFT can be used to show the compression of the spectrum as the musclefatigue. The WVD has cross-terms and therefore is not a preciserepresentation of the changing of the frequency components with fatigue.When walls appear in the Choi-William distribution, there is a spike inthe original signal. It will decide if the walls contain any significantinformation for the study of muscle fatigue. In another embodiment, theautoregressive (AR) time series model can be used to study EMG signal.In one embodiment, neural networks can process EMG signal where EMGfeatures are first extracted through Fourier analysis and clusteredusing fuzzy c-means algorithm. Fuzzy c-means (FCM) is a method ofclustering which allows data to belong to two or more clusters. Theneural network output represents a degree of desired muscle stimulationover a synergic, but enervated muscle. Error-back propagation method isused as a learning procedure for multilayred, feedforward neuralnetwork. In one implementation, the network topology can be thefeedforward variety with one input layer containing 256 input neurodes,one hidden layer with two neurodes and one output neurode. Fuzzy logicsystems are advantageous in biomedical signal processing andclassification. Biomedical signals such as EMG signals are not alwaysstrictly repeatable and may sometimes even be contradictory. Theexperience of medical experts can be incorporated. It is possible tointegrate this incomplete but valuable knowledge into the fuzzy logicsystem, due to the system's reasoning style, which is similar to that ofa human being. The kernel of a fuzzy system is the fuzzy inferenceengine. The knowledge of an expert or well-classified examples areexpressed as or transferred to a set of “fuzzy production rules” in theform of IF-THEN, leading to algorithms describing what action orselection should be taken based on the currently observed information.In one embodiment, higher-order statistics (HOS) is used for analyzingand interpreting the characteristics and nature of a random process. Thesubject of HOS is based on the theory of expectation (probabilitytheory).

In addition to stroke detection, EMG can be used to sense isometricmuscular activity (type of muscular activity that does not translateinto movement). This feature makes it possible to define a class ofsubtle motionless gestures to control interface without being noticedand without disrupting the surrounding environment. Using EMG, the usercan react to the cues in a subtle way, without disrupting theirenvironment and without using their hands on the interface. The EMGcontroller does not occupy the user's hands, and does not require themto operate it; hence it is “hands free”. The system can be used ininteractive computer gaming which would have access to heart rate,galvanic skin response, and eye movement signals, so the game couldrespond to a player's emotional state or guess his or her level ofsituation awareness by monitoring eye movements. EMG/EEG signal can beused for man-machine interfaces by directly connecting a person to acomputer via the human electrical nervous system. Based on EMG and EEGsignals, the system applies pattern recognition system to interpretthese signals as computer control commands. The system can also be usedfor Mime Speech Recognition which recognizes speech by observing themuscle associated with speech and is not based on voice signals but EMG.The MSR realizes unvoiced communication and because voice signals arenot used, MSR can be applied in noisy environments; it can supportpeople without vocal cords and aphasics. In another embodiment, EMGand/or electroencephalogram (EEG) features are used for predictingbehavioral alertness levels. EMG and EEG features were derived fromtemporal, frequency spectral, and statistical analyses. Behavioralalertness levels were quantified by correct rates of performance on anauditory and a visual vigilance task, separately. A subset of three EEGfeatures, the relative spectral amplitudes in the alpha (alpha %, 8-13Hz) and theta (theta %, 4-8 Hz) bands, and the mean frequency of the EEGspectrum (MF) can be used for predicting the auditory alertness level.

In yet a further embodiment for performing motor motion analysis, an HMMis used to determine the physical activities of a patient, to monitoroverall activity levels and assess compliance with a prescribed exerciseregimen and/or efficacy of a treatment program. The HMM may also measurethe quality of movement of the monitored activities. For example, thesystem may be calibrated or trained in the manner previously described,to recognize movements of a prescribed exercise program. Motor functioninformation associated with the recognized movements may be sent to theserver for subsequent review. A physician, clinician, or physicaltherapist with access to patient data may remotely monitor compliancewith the prescribed program or a standardized test on motor skill. Forexample, patients can take the Wolf Motor Function test and accelerationdata is captured on the following tasks:

-   -   placing the forearm on a table from the side    -   moving the forearm from the table to a box on the table from the        side    -   extending the elbow to the side    -   extending the elbow to the side against a light weight    -   placing the hand on a table from the front    -   moving the hand from table to box    -   flexing the elbow to retrieve a light weight    -   lifting a can of water    -   lifting a pencil, lifting a paper clip    -   stacking checkers, flipping cards    -   turning a key in a lock    -   folding a towel lifting a basket from the table to a shelf above        the table.

All references including patent applications and publications citedherein are incorporated herein by reference in their entirety and forall purposes to the same extent as if each individual publication orpatent or patent application was specifically and individually indicatedto be incorporated by reference in its entirety for all purposes. Manymodifications and variations of this invention can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only. The above specification, examples anddata provide a complete description of the manufacture and use of thecomposition of the invention. Since many embodiments of the inventioncan be made without departing from the spirit and scope of theinvention, the invention resides in the claims hereinafter appended.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the spirit and scope of theinvention as defined by the appended claims.

What is claimed is:
 1. A heart monitoring system for a patient, comprising: one or more wireless nodes forming a wireless network; a body wearable appliance including a device to pick up electric or optical signal, said appliance having a wireless transceiver to communicate with the one or more wireless nodes; and an analyzer to determine vital signs, the analyzer coupled to the wireless transceiver to receive patient data over the wireless network.
 2. The system of claim 1, wherein the analyzer comprises one of: a Hidden Markov Model (HMM) recognizer, a dynamic time warp (DTW) recognizer, a neural network, a fuzzy logic engine, a Bayesian network.
 3. The system of claim 1, wherein the appliance comprises one of: a differential amplifier, an accelerometer, an EMG detector, EEG detector, an EKG detector, an ECG detector, an electromagnetic detector, an ultrasonic detector, an optical detector.
 4. The system of claim 1, comprising a sound transducer coupled to the wireless transceiver to communicate audio through the network.
 5. The system of claim 1, wherein the appliance monitors patient movement.
 6. The system of claim 1, comprising an in-door positioning system coupled to one or more network appliances to provide location information.
 7. The system of claim 1, comprising a call center coupled to the appliance to provide a human response.
 8. The system of claim 1, comprising a web server coupled to the network to provide information to an authorized remote user.
 9. The system of claim 1, wherein the analyzer determines deteriorations in vital signs.
 10. The system of claim 1, wherein the analyzer determines one of: blood pressure, glucose, EEG, ECG, calorie, patient activity.
 11. The system of claim 1, wherein the appliance transmits and receives voice from the person over the network.
 12. The system of claim 1, comprising a body impedance (BI) sensor to determine one of: total body water, compartmentalization of body fluids, cardiac monitoring, blood flow, skinfold thickness, dehydration, blood loss, wound monitoring, ulcer detection, deep vein thrombosis, hypovolemia, hemorrhage, blood loss, heart attack, stroke attack.
 13. The system of claim 1, wherein the wireless network continuously communicates vital signs throughout a hospital to make vital signs accessible to authorized hospital employees.
 14. The system of claim 1, wherein the appliance transmits and receives voice from the person over the network to one of: a doctor, a nurse, a medical assistant, a caregiver, an emergency response unit, a family member.
 15. The system of claim 1, comprising code to store and analyze patient information.
 16. The system of claim 1, wherein the appliance captures medicine taking habits, eating and drinking habits, sleeping habits, or excercise habits.
 17. The system of claim 1, comprising a housing having one or more bioelectric contacts coupleable to the patient, the housing selected from one of: a patch, a wristwatch, a band, a wristband, a chest band, a leg band, a sock, a glove, a foot pad, a head-band, an ear-clip, an ear phone, a shower-cap, an armband, an ear-ring, eye-glasses, sun-glasses, a belt, a sock, a shirt, a garment, a jewelry, a bed spread, a pillow cover, a pillow, a mattress, a blanket, each having one or more sensors in communication with the wireless network.
 18. A system, comprising: one or more wireless nodes forming a wireless network; a body wearable appliance including a conductor, a cover, a clip, a wearable band, a patch or an adhesive band to pick up electric or optical signal, said appliance having a wireless transceiver to communicate with the one or more wireless nodes; and a computer to determine fitness, the computer coupled to the wireless transceiver to receive fitness data over the wireless network.
 19. A monitoring system for a person, comprising: one or more wireless nodes; and a body wearable sensor including a device to pick up electric or optical signal, said sensor coupled to the person to capture fitness data from the person, the sensor coupled to the wireless nodes to communicate patient data.
 20. The system of claim 19, wherein the sensor captures motion, acceleration or continuous blood pressure measurement for the person. 